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Der Verschluss ist eine Baugruppe eines Hinterladers und schließt den Lauf nach hinten ab. Der Verschluss verhindert das Austreten von Treibladungsgasen nach hinten. Er muss dabei stabil genug sein, um dem Druck dieser Gase standzuhalten. Bei Waffen für Patronen- oder Kartuschenmunition werden die Patronen bzw. Kartuschen durch den Verschluss im Lauf fixiert und die Abdichtung wird durch Liderung des Hülsenmaterials erreicht.

Als Verschlusssystem kann der Verschluss weitere Funktionen wie Laden, Abfeuern, Sichern und Entladen der Waffe übernehmen.

Geschichte

Schon zur Zeit der Vorderladerwaffen wurde die Notwendigkeit erkannt, an den zu damaliger Zeit fest verschlossenen Teil des Laufes zu gelangen, in dem das Pulver gezündet werden sollte, um die vor dem Pulver platzierte Kugel aus dem Lauf zu verschießen. Zündete das Pulver nicht, war das Entfernen der Kugel aus dem Lauf ein umständliches Unterfangen. Die Lösung für dieses Problem war der Vorläufer der Feuerwaffenverschlüsse, die Schwanzschraube, die den Lauf nach hinten verschloss, oder die Entwicklung von Kammergeschützen mit abnehmbarer Kammer für die Ladung.

Einen Zwischenschritt in der Entwicklung moderner Verschlusssysteme bildete das 1827 von Johann Nikolaus von Dreyse entwickelte Zündnadelsystem, dessen weitere Entwicklung zu einem schon in den Jahren von 1839 bis 1840 von der preußischen Armee getesteten Hinterladergewehr führte.

Ein anderer Weg wurde in Bayern beschritten: Dort wurden Vorderlader-Perkussionsgewehre zu Hinterladergewehren mit Perkussionszündung umgebaut. So wurde das "Podewils-Lindner-Gewehr M1858/67" 1861 als Vorderlader "M/58/II" von Auguste Francotte & Cie in Lüttich gebaut, etwa 1867 in Amberg zum Hinterlader umgebaut und in "M/58/67 II" umbenannt. Der Verschluss besteht aus einer Schraubenverriegelung mit einem aufgeschnittenen Gewinde und Staubschutzdeckel, der von Edward Lindner entwickelt wurde. Die Perkussionszündung blieb so gut wie unverändert erhalten.

Der Vorteil der Hinterladergewehre war ihre höhere Schusskadenz und die Möglichkeit, diese Waffen im Liegen zu laden, ohne die Deckung aufgeben zu müssen. Vor der Einführung von Metall-Patronenhülsen war die mangelnde Gasdichtigkeit der damaligen Verschlüsse ein Problem, siehe Chassepotgewehr und Dreyse-Zündnadelgewehr. Durch die Metall-Patronenhülsen konnte die Treibladung verstärkt werden, da die durch den Gasdruck hervorgerufene Ausdehnung der Patronenhülse im Patronenlager zu einer Liderung führt, die das Patronenlager nach hinten gasdicht abschließt. Mit der Einführung der raucharmen Munition mussten die Verschlusssysteme verbessert werden. Im Fall der in Armeegewehren meist verwendeten Zylinderverschlüsse wurden die Verriegelungselemente vorn am Verschlusskopf angebracht, was erlaubte, den Verschluss direkt in der Laufverlängerung zu verriegeln.

Verschlusstechnik
Der Verschluss muss beim Schuss den hohen Kräften standhalten, die der Gasdruck der Treibladung ausübt, um einerseits die Funktion der Waffe zu gewährleisten und andererseits eine Gefährdung des Schützen durch ausströmende Gase oder eine Aufsprengung auszuschließen. Bei der Berechnung der Verriegelungselemente muss der bei der Verbrennung entstehende Spitzendruck eingesetzt werden. Bei einem Spitzendruck von 1000 bar und einer beschlagenen Verschlussfläche von 1 cm² beträgt die wirkende Kraft 10.000 N (ca. 1 t). Die Abdichtung nach hinten erfolgt bei modernen Waffen in erster Linie durch die Liderung der Patronenhülse, wobei die an die Hülse angepasste vordere Fläche des Verschlusskopfes, der Stoßboden, den Patronenboden nach hinten abstützt. Verschlüsse von Waffen mit hülsenloser Munition werden formschlüssig abgedichtet.

Bis auf Vorderladerwaffen und Revolver verfügen praktisch alle Arten von Feuerwaffen über einen Verschluss. Der Verschluss selbst besteht gegebenenfalls aus verschiedenen einzelnen Teilen und bildet somit ein Verschlusssystem. Beispielsweise sind unter anderem folgende Teile in Verschlusssystemen zu finden:
 Schlagbolzen, auch separates Schlagstück
 Schlagbolzenfeder
 Auszieher (Auszieherkralle)
 Gasabnahme (bei Verschlussentlastungen automatischer Waffen)
 Sicherung (verschiedene Schlagbolzensicherungen und Schlagstücksicherungen)

Revolver benötigen bis auf wenige Ausnahmen (z. B. gasdichte Revolver) keine Verschlussmechanismen. Die Trommel bildet das Magazin und auch das Patronenlager, das vom Lauf separiert ist. Die Abdichtung erfolgt durch die Liderung der Patronenhülse, die hintere Unterstützung des Patronenlagers und der darin enthaltenen Patrone wird durch den Rahmen des Revolvers gewährleistet.

Verriegelungstechnik

Grund für die Verriegelung
Ein Verschluss muss während der Schussabgabe die Abdichtung beziehungsweise die Abstützung der Patrone aufrechterhalten und darf sich nicht öffnen, bevor der Gasdruck auf einen ungefährlichen Wert abgesunken ist. Bei Selbstladewaffen kommen auch unverriegelte Verschlüsse zum Einsatz, bei denen das Öffnen des Verschlusses durch seine Masse kraftschlüssig verzögert wird. Eine Verriegelung erlaubt jedoch die Verwendung wesentlich leistungsstärkerer Munition.

Unverriegelter Verschluss (bei Selbstladern mit der sogenannten Sicherheitsstrecke)
Der unverriegelte Verschluss (auch als Masseverschluss oder gefederter Masseverschluss bezeichnet) beruht auf der Massenträgheit eines relativ massiv gehaltenen Verschlusses. Der Verschluss wird beim Schuss durch den Rückstoß in Bewegung gesetzt, wobei diese Rückwärtsbewegung langsam genug erfolgt, um sicherzustellen, dass die Hülse erst nach dem ausreichenden Absinken des Gasdruckes ganz aus dem Patronenlager ausgezogen wird. Die Kraft der Rückholfeder hat bei diesen Systemen kaum Einfluss auf das Öffnungsverhalten unmittelbar nach der Schussabgabe. Im Bereich der Handfeuerwaffen setzen Verschlussmasse und Federspannung der Leistung der verwendeten Munition enge Grenzen. Ab einer bestimmten Leistungsklasse der Munition muss der Verschluss relativ schwer ausgeführt werden (vgl. Uzi oder Sten Gun), oder es muss eine sehr starke Schließfeder verwendet werden (vgl. "Le Francaise" und alte "Astra"-Waffen), was die Waffenhandhabung erschwert. Im Bereich größerkalibriger Waffen kamen unverriegelte Masseverschlüsse etwa bei den Bordwaffen MK 108 und MG FF zum Einsatz.

Verriegelter Verschluss (bei Selbstladern mit der sogenannten Unterstellstrecke)
Beim verriegelten Verschluss stellen massive Verriegelungselemente die Verbindung zwischen Lauf und Verschluss beim Schuss sicher.

Beispiele:
 Kämme auf dem Lauf, entsprechende Nuten im Schlitten, Browning-System, Colt 1911, Lauf kippt ab zum Entriegeln.
 Verschlussblock am Laufende, verriegelnd im Auswurffenster des Schlittens, Glock-Pistolen, SIG 220. Lauf kippt ab zum Entriegeln.
 Kämme auf dem Lauf, entsprechende Nuten im Schlitten, Steyr M1912, Beretta 8000, Beretta Px4 Storm, Boberg XR9-S, Obregon-Pistole (Mexiko), Lauf dreht sich zum Entriegeln.
 Warzen am Verschlusszylinder, Mauser System 98 und viele andere
 Drehriegelverschluss: eine drehbare Hülse umschließt Kammer und Verschluss. Auf der Innenseite der Hülse sind Verriegelungskämme angebracht, die in die entsprechenden Gegenstücke im Verschluss greifen. MG 30 und Abkömmlinge, Solothurn S18/100
 Drehkopfverschluss, M16 und G36, AK 47, Mehrlade- und Selbstladeflinten
 Stützklappen, Sauer 80, Mg 51, Browning Automatic Rifle,
 Kugelmechanismus, Heym SR 30
 Schwenkriegel, Pistole Walther P.38 / P1 oder Beretta 92F
 Vertikalblock, Pistole Lahti35, ARSENAL FIREARMS AF-1 (Strike One, Stryk B)
 Kniegelenk, Maxim-Maschinengewehre, Borchardt- und Luger-Pistolen, frühe Winchester-Gewehre.

Verzögerter Masseverschluss
Ein nicht starr verriegelter Masseverschluss einer Automatikwaffe, bei dem der Verschlusskopf beim Schuss durch einen geeigneten Mechanismus den Rücklauf verzögert, heißt Verzögerter Masseverschluss.

Beispiele:
 Rollenverschluss, H&K-G3-Gewehr, Sturmgewehr 57
 Hebelübersetzung, FAMAS (Hebelübersetzung)
 Kraftübertragung durch gleitendes Element, Thompson-Maschinenpistole
 Kniegelenk, Maschinengewehr Schwarzlose

Zeitliche Verschlusssteuerung
Zusätzlich zum Zeitpunkt muss der Ablauf des Ver- und Entriegelns des Verschlusses mechanisch gesteuert werden. Hierfür existiert eine Reihe unterschiedlicher konstruktiver Lösungen. Die weitaus meisten Lösungen basieren darauf, dass Verschluss und Lauf beim Schuss zunächst eine gemeinsame Bewegung nach hinten antreten und sich beim Erreichen eines definierten Punktes (z. B. einer Klinke oder Steuerkurve) trennen, d. h. die Verriegelung wird aufgehoben. Eine besondere Lösung dieses Problems zeigt der "unterknickte Kniegelenkverschluss" der Pistolenfamilie der 08-Pistolen von Georg Luger (auch: Parabellumpistole). Danach bleibt der Lauf stehen, während der Verschluss seine Rückwärtsbewegung so lange fortsetzt, bis die Kraft der Schließfeder ihn wieder nach vorn treibt. Gemeinsam treten Lauf und Verschluss sodann in ihre Ruhelage zurück, wobei die Verriegelung eintritt.

Die Steuerung des Mechanismus der Verschluss- und Verriegelungssysteme wird beim Schuss entweder über die Rückstoßenergie auf den Stoßboden des Verschlusses oder durch Rücklauf des gesamten Systems (Lauf mit Verschluss wie z. B. bei Kanonen) oder auch per Gasdruck durch eine mechanische Übertragung der Energie des beim Schuss in alle Richtungen wirkenden Gasdrucks auf das Verschluss- und Verriegelungssystem bewerkstelligt (siehe Gasdrucklader).

Während bei Selbstladegewehren beide Systeme vorkommen und die Gasdrucklader unter den modernen Waffen verbreiteter sind, funktionieren verriegelte Selbstladepistolen – bis auf wenige Ausnahmen – als Rückstoßlader (Rückstoß wie Rücklauf).

Verschlusssysteme
Es werden allgemein unverriegelte und verriegelte Verschlusssysteme unterschieden. Unverriegelte Verschlusssysteme arbeiten kraftschlüssig und finden vorwiegend bei Waffen im Kleinkaliber oder mit kleinkalibriger Munition Verwendung; die Ausnahme bilden Maschinenpistolen. Verriegelte Verschlusssysteme arbeiten formschlüssig und sind in Waffen zum Verschießen starker Munition unumgänglich.

Kipplaufverschluss

Bei dieser Verschlussart kann der Lauf um eine Drehachse gekippt werden und gibt so das Patronenlager frei, so dass eine Patrone eingelegt beziehungsweise entnommen werden kann. Der Verschluss ist Teil des Systemgehäuses (Basküle) und ist heute die bei weitem häufigste Form des Baskülverschlusses, der bei Flinten und kombinierten Waffen eingesetzt wird. Seit dem 18. Jahrhundert wurden Kipplaufverschlüsse für Hinterlader konstruiert. Der Kipplaufverschluss gehört bei modernen Jagdwaffen und Waffen für das sportliche Flintenschießen zu den am meisten verwendeten Konstruktionen. Beim weit verbreiteten Greener-System – auch Greenerverschluss bekannt ,– greifen Verschlusskeile in Haken unterhalb des hinteren Laufendes und verriegeln den Verschluss. Ebenfalls bekannt ist der Kerstenverschluss und der Doppelriegelverschluss. Beim auf dem Bild gezeigten Verschluss ist am oberen Laufende ein zusätzliches Verriegelungselement angebracht. Die Ver- und Entriegelung geschieht manuell durch den Schützen über einen Bedienhebel; die Patronen werden beim Öffnen des Verschlusses durch den Auszieher ein Stück aus dem Patronenlager gehoben. Bei einigen Modellen werden leere Patronenhülsen mittels eines federgetriebenen Auswerfers (Ejektor) ausgeworfen.

Moderne Waffen mit Kipplaufverschluss haben meistens einen bis vier Läufe. Bei mehrläufigen Waffen existieren zahlreiche Bauformen mit verschiedenen Laufanordnungen und Kombinationen von Kalibern.

Kipplaufrevolver bilden das Bindeglied zwischen Revolverkonstruktion und dem Kipplaufverschluss.

Blockverschluss

Klappenverschluss

Der Klappenverschluss ist eine sehr alte Variante eines Verschlusses für Handfeuerwaffen. Entsprechende Einzelstücke sind aus dem 16.- oder gar 15. Jahrhundert bekannt. Den ersten modernen Klappenverschluss mit einer Einheitspatrone konstruierte Sylvestr Krnka im Jahre 1849.

Bei Waffen mit Klappenverschluss handelt es sich oft um umgebaute Vorderlader. Grund ist die Kürze des Verschlusssystems, das problemlos im hinteren Bereich des Laufes neben dem Schloss angebracht werden kann. Beim nach oben zu öffnenden Klappenverschluss liegt das Scharnier vorne über dem Laufende. Blockiert wird der Verschluss mit einem hinten am Block schwenkbar angebrachten Keil. Der im Verschlussblock gelagerte Zündstift wird durch eine Feder nach hinten gedrückt, die Zündung erfolgt in der Regel durch das an der Waffe angebrachte ursprüngliche Perkussionsschloss, der Hahn schlägt auf den Zündstift. Typische Beispiele von Waffen mit Klappenverschluss sind die Springfield-Trapdoor-Gewehre der US-Armee und die schweizerischen Milbank-Amsler-Gewehre.

Fallblockverschluss

Beim Fallblockverschluss ist der Verschlussblock im Verschlussgehäuse senkrecht verschiebbar eingesetzt. Zum Laden wird er mittels einer Hebelmechanik nach unten gezogen, um das Patronenlager freizugeben. Siehe auch Sharps Rifle. Beim System Martini ist der Verschlussblock hinten angelenkt und wird zum Laden abgekippt, indem der Abzugsbügel heruntergezogen wird.

Die Bergmann-Pistole, die Lahti-35 sowie die StrikeOne sind verriegelte Rückstoßlader und verwenden diese Verschlussart.

Rolling-Block-Verschluss

Beim Drehblockverschluss wird der achsgelagerte Verschlussblock durch den Hahn verriegelt. Zum Laden wird der Hahn gespannt und der Verschlussblock nach hinten abgekippt. Die Patrone wird in das offene Patronenlager eingeschoben. Daraufhin wird der Verschlussblock hochgeklappt, er wird durch eine Feder am Öffnen gehindert. Wird der Abzug betätigt, verriegelt der vorschnellende Hahn den Verschluss und zündet die Patrone.

Kippblockverschluss
Der Kippblockverschluss, der vorzugsweise in Gasdruckladern wie beispielsweise dem Bren-Maschinengewehr, dem Tokarew-SWT-40-Gewehr und der MP 44 Verwendung findet, ist eine moderne Weiterentwicklung der Blockverschlüsse.

Tabernakelverschluss
Der Tabernakelverschluss gehört konstruktiv zu den Blockverschlüssen und wird genauer als Wellenblockverschluss mit Lademulde bezeichnet.

Zylinderverschluss

Der Zylinderverschluss wird auch als Kammerverschluss bezeichnet. Er wird in diversen Varianten hergestellt, das Prinzip der Verriegelung ist jedoch immer dasselbe. Zum Entriegeln muss der Verschluss mit dem Kammerstängel gedreht werden, bevor er geöffnet werden kann. Die Verriegelung erfolgt entweder über meist zwei Verriegelungselemente (Zapfen oder Gewindekämme) am Verschluss, die in entsprechende Aussparungen im Verschlussgehäuse greifen. Bei Kleinkaliberwaffen erfolgt sie oft nur über den Kammerstängel, der in eine entsprechende Aussparung seitlich in der Systemhülse greift. Am weitesten verbreitet und kopiert ist das Mauserschloss (Mauser System 98, K98), das noch heute als Endpunkt eines Prinzips gelten kann.

Die Verriegelungselemente sind nicht senkrecht zur Drehachse, sondern in einem Winkel leicht schraubenförmig angebracht. Die Drehung beim Entriegeln des Verschlusses bewirkt folglich einen Rücklauf von 1 bis 2 mm desselben, was über den Auszieher die Hülse lockert. Diese primäre Extraktion vermindert den Kraftaufwand zum Nachladen erheblich.

Eine Variante sind die Geradezugverschlüsse. Bei diesen erfolgt die Drehung der Kammer oder der zur Kammer gehörenden Verriegelungshülse nicht direkt über den Kammerstängel, sondern über einen Ladehebel, der die Kammer oder Verriegelungshülse über eine entsprechend eingefräste Kulisse in Drehung versetzt. Siehe auch Drehkopfverschluss.

Kniegelenkverschluss

Beim Kniegelenkverschluss wird die Kammer (Verschluss) durch ein gestrecktes Kniegelenk am Rücklauf gehindert. Bei Repetierwaffen (Winchester (Gewehr)) wird zum Nachladen das Kniegelenk durch die Betätigung des Ladehebels geknickt, der Verschluss läuft zurück, zieht die Patronenhülse aus und schiebt im Vorlauf die neue Patrone in das Patronenlager.

Bei Selbstlade- und Automatwaffen ist das Kniegelenk vor der Schussabgabe überstreckt, so dass der Verschluss sicher verriegelt ist. Bei der Schussabgabe beschleunigt der Rückstoß den Lauf zusammen mit dem Verschlusssystem nach hinten. Während des Rücklaufes wird das Kniegelenk durch eine Steuerkurve geknickt, der Verschluss läuft infolge seiner Massenträgheit und des noch wirkenden Restdruckes im Patronenlager weiter zurück, während der Lauf gestoppt wird. Im Rücklauf wirft der Verschluss die leere Hülse aus. Anschließend wird er durch die Schließfeder wieder nach vorn beschleunigt und führt eine neue Patrone aus dem Magazin oder Patronengurt ins Patronenlager ein.

Die Parabellumpistole ist ein verriegelter Rückstoßlader mit dieser Verschlussart.

Eine Sonderform des Kniegelenkverschlusses findet sich beim 1923 in Amerika entwickelten halbautomatischen Pedersen-Gewehr. Bei diesem ist das Kniegelenk nicht überstreckt, sondern minimal geknickt, was dazu führt, dass es durch den Rückstoß verzögert geöffnet wird. Das System hat sich nicht durchsetzen können, da eine sichere Funktion gefettete Patronen voraussetzte.

Schwenkriegelverschluss

Beim Schwenkriegelverschluss  wird der Rücklauf des Verschlusses durch einen Riegel verhindert, der sich durch sein Abklappen am Verschlussgehäuse abstützt.
Ab 1881 konstruierte die französische Firma Darne in St. Etienne neben konventionellen Kipplauf-Doppelflinten solche mit einem Schwenkriegelverschluss. Zum Nachladen wurde dieser mit einem oben angebrachten Hebel betätigt.
Eine frühe Anwendung fand dieses Verriegelungssystem auch beim von Ferdinand Ritter von Mannlicher entwickelten Repetiergewehr System Mannlicher mit Geradezugverschluss sowie den Ordonnanzgewehren Modell 1885 und M1886 der Landstreitkräfte Österreich-Ungarns.

Auch beim von John Moses Browning 1918 entwickelten Gasdrucklader, dem Browning Automatic Rifle, wird der Verschluss durch einen Schwenkriegel am Rücklauf gehindert.

Die Mauser C96, Walther P.38 / P1 und Beretta 92 sind verriegelte Rückstoßlader, bei denen der Schwenkriegel die Verbindung von Lauf und Verschluss nach kurzem Zurückgleiten löst.

Stützklappenverschluss
Beim Stützklappenverschluss wird die Verriegelung von Verschluss und Lauf durch zwei horizontale Klappen, die in zwei einander gegenüberliegende Aussparungen der Systemhülse greifen, erzeugt. Gesteuert wird die Ver- bzw. Entriegelung entweder durch Steuerkurven oder einen entsprechend geformten Bolzen, der mit dem Verschlussträger verbunden ist. Bekannte Waffen mit dieser Verriegelung sind die Wehrmachtsgewehre G 41 und G 43 sowie das leichte Maschinengewehr DP-27 der Sowjetunion und dessen Nachfolgern (z. B. RPD). Aktuell wird das System z. B. von Sauer (Repetierer des Systems 90) und  Alexander Arms (im Selbstladegewehr "Ulfberht" im Kaliber .338 Lapua Magnum) verwendet.

Ein ähnliches Verriegelungssystem mit derselben Bezeichnung wurde Anfang des 20. Jahrhunderts für Waffen der Firma Mauser entwickelt. Die Stützklappen waren bei der Pistole 06/08 und dem Selbstladegewehr M1915 jedoch hinter dem Verschluss, bei der Pistole 1912/14 dagegen unter dem feststehenden Lauf angeordnet.

Besondere Verschlusssysteme
Weitere Verschlusssysteme finden sich insbesondere bei Geschützen. Während die vorbeschriebenen Verschlusssysteme vorwiegend bei Handfeuerwaffen anzutreffen sind, finden sich für Geschütze weitere technische Lösungen zur Verschlussgestaltung, die teilweise mit obigen Verschlusssystemen verwandt sind. Darunter sind beispielsweise zu nennen:

 Keilverschluss
 Kolbenverschluss
 Kugelverschluss
 Schraubenverschluss

Verschlusssysteme automatischer Waffen
Verschlüsse selbstladender Waffen werden nach der Abgabe des Schusses automatisch geöffnet, um die abgeschossene Hülse auszuwerfen und das Schlagstück zu spannen. Das Öffnen des Verschlusses darf auch hier erst dann erfolgen, wenn der Gasdruck im Lauf auf einen ausreichend niedrigen Wert gesunken ist. Dies ist kurz nach dem Austritt des Geschosses aus der Laufmündung der Fall. Die meisten selbstladenden Waffen sind so gerechnet, dass die Massenträgheit der beschleunigten Komponenten genügt, um die Schließfeder zu spannen, damit diese den Nachladezyklus abschließen kann. Die "Verschlusssteuerung" in Abhängigkeit von der verwendeten Munition bildet also ein zentrales konstruktives Problem für den Bau von Selbstladern.

Masseverschluss

Der Masseverschluss ist ein unverriegelter Verschluss für automatische Waffen, der die Verschlussfunktion aufgrund seiner eigenen Masse – im Verhältnis zur Stärke der mit ihm verwendeten Patronenmunition – gewährleistet. Die Masse des Verschlusses ist dabei so ausgelegt, dass sie eine sichere Schussabgabe erlaubt und der nach hinten auf den Verschluss wirkende Gasdruck ausreichend ist, um den Repetiervorgang (das Auswerfen der abgeschossenen Patronenhülse, Spannen des Schlagstücks und Abzugssystems sowie das Nachladen der Waffe mit einer neuen Patrone) durchzuführen. Die Verschlusssteuerung erfolgt hierbei über die Massenträgheit des Verschlusses. Verwendung findet dieses System in automatischen Kleinkalibergewehren, Selbstladepistolen sowie Maschinenpistolen. Für Munition mit nicht zylindrischen Hülsen ist das System wegen Gasverlust und Hülsenreißern ungeeignet.

Browning-Verriegelungssystem

Zur Verriegelung sind beim Browning-System Lauf und Verschluss (Schlitten) am hinteren Laufende mittels zweier Verriegelungskämme miteinander verbunden. Nach der Schussauslösung laufen beide gemeinsam zurück; das mit dem Griffstück über ein Kettenglied verbundene Laufende wird durch dieses nach unten gezogen. Die Verbindung zwischen Lauf und Verschluss wird damit gelöst und der Verschluss läuft frei weiter zurück. Am hinteren Totpunkt angelangt, wird der Verschluss durch die Schließfeder zum Nachladen nach vorne gebracht und trifft auf den Lauf. Dieser wird durch das Kettenglied nach oben in seine verriegelte Ausgangsposition gebracht. Die Waffe ist verriegelt und schussbereit.

Rollenverschluss

Beim Rollenverschluss kommen als Rollen bezeichnete Walzen als Verriegelungselement zum Einsatz. Dabei liegen diese Walzen auf der einen Seite in sogenannten Walzentaschen im Schildzapfen der Waffe. Auf der anderen Seite liegen die Walzen auf dem sogenannten Steuerstück auf, welches Teil des Verschlussträgers ist. Dazwischen liegt meist der Verschlusskopf welcher zwei Nuten aufweist, in welchen sich die Rollen vertikal bewegen können.

Rollenverschlüsse können formschlüssig statisch ausgeführt sein, indem die Rollentaschen so tief liegen, dass die Rollen sich nicht unter der Kraft der Stoßbodenkräfte daraus befreien können. Auch kann das Steuerstück vertikale Auflageflächen aufweisen und so die Rollen formschlüssig verriegeln. Bei als Antriebssystem von formschlüssigen Rollenverschlüssen wird entweder der Rückstoß (z.b. MG42, Pistole vz. 52) verwendet oder aus dem Lauf abgezapfter Gasdruck (zb. Mauser Gerät-06).

Im Gegensatz dazu stehen Rollenverschlüsse, welche kraftschlüssig dynamisch verriegelt sind und sich selbstständig unter der Kraft der Stoßbodenkräfte öffnen können. Dabei wird der Verschlusskopf nach hinten gedrückt, wobei zeitgleich diese Bewegung über die Rollen auf den Verschlussträger übertragen wird. Dabei findet eine Übersetzung einer schweren Masse ins Schnellere statt, diese Übersetzung erzeugt einen mechanischen Nachteil, welche die Öffnungsbewegung des Verschlusskopfes verlangsamt. Da die Verriegelung durch diese abgeschleuderte Masse bewerkstelligt wird, zählt man diesen Verschluss zu den übersetzten Masseverschlüssen. Bekannte Vertreter dieser Art des Rollenverschlusses sind das Schnellfeuergewehr G3, die Maschinenpistole MP5, die Selbstladepistole P9 und das Sturmgewehres 57 der Schweizer Armee.

Gasgebremster Verschluss
Bei gasgebremsten Verschlüssen wird ein Teil des Druckes im Lauf durch Bohrungen auf eine Kolbenfläche umgeleitet, welche dem Rücklauf des Verschlusses entgegenwirkt. Daraus resultiert, dass die Masse des Verschlusses im Gegensatz zu Waffen mit einem reinen Masseverschluss geringer gehalten werden kann. Zudem bewirkt ein höherer Gasdruck im Lauf einen erhöhten Druck auf der Kolbenfläche. Dieser Druckausgleich führt dazu, dass die Waffe mit verschiedenen Ladungen einwandfrei funktioniert. Typische Waffen mit "gasgebremsten" Verschlüssen sind das deutsche Volkssturmgewehr VG 1–5 von 1945, die Pistole Steyr GB, die Pistolenbaureihe HK P7 und die Pistole Walther CCP.

Weblinks
 Informationen zu Vorläufern moderner Verschlusssysteme
 Verschlüsse der Geschütze in Lueger: Lexikon der gesamten Technik (1904)

Literatur
 Lueger 1904 Eintrag: Jagdgewehre
 Meyers 1905 Eintrag: Jagdgewehr
 Willi Barthold: Jagdwaffenkunde. 3. bearbeitet Auflage. VEB Verlag Technik, Berlin 1979.
 Peter Dannecker: Verschlußsysteme von Feuerwaffen. 3. überarbeitete und erweiterte Auflage. dwj-Verlag, Blaufelden 2009, ISBN 978-3-936632-20-0.
 Vladimír Dolínek: Illustriertes Lexikon der Handfeuerwaffen. K. Müller, Erlangen 1998, ISBN 3-86070-773-6.

 Jaroslav Lugs: Handfeuerwaffen. Systematischer Überblick über die Handfeuerwaffen und ihre Geschichte. 2 Bände. 8. Auflage. Militärverlag der DDR, Berlin 1986, ISBN 3-327-00032-8.
 Craig Philip: Enzyklopädie der Handfeuerwaffen. Karl Müller, Erlangen 1995, ISBN 3-86070-499-0.
 W. H. B. Smith, Joseph E. Smith: Small Arms of the World. The basic Manual of military small Arms. American – British – Russian – German – Italian – Japanese, and all other important Nations. 5th edition, revised and enlarged, 3. printing. Military Service Publishing Co., Harrisburg PA 1957.

Einzelnachweise

Komponente (Waffentechnik)
Bauteil (Handfeuerwaffe)

redpajama_set_name: RedPajamaWikipedia


In the first part of this series, we talked about being "high-centered", stuck between in the rut and ridge of a bumpy competition-conflict relationship, unable to make progress towards collaboration-partnership. Sometimes, no matter how hard you try, you go from stuck, to sideways, to upside-down. Let's talk about the latter two.
Sideways and what to try. Let's say you've been working in good faith with a partner, along the lines of the first blog in this series. You've been sharing credit, getting ahead of rumors, agreeing on talking points, and building trust. But maybe you've heard that a partner is still posturing as an opponent when they interact with particular audiences. This is what I mean by going sideways.
Remember that, while you can't control your partner, you can control your reaction to the situation. This includes how you frame what your partner is doing or saying. That latter point is important when talking with leadership and others in your agency about what's happening. You may have to talk your own people "off the ledge" if they feel like your agency is being thrown under the bus by a partner. As I mentioned in Part 1, that perception, deserved or not, can become a problem if allowed to fester. Use your reaction to this situation as an opportunity to get ahead of the rumor mill.
Reach out to the partner and discuss the situation. You might do this in the same way one might work with an employee. Start by recognizing your own assumptions and suspending those first. In the best case, maybe there's a reasonable explanation or a simple misunderstanding. Reach out to the partner, share what you've heard and why you're concerned, with a clear focus on how the process is being affected. Speak professionally and firmly, with a goal of understanding the issue and reaching an agreement—even if informal—about future communications. That's where having already modeled the desired behavior is invaluable. And remember the other Golden Rule: Speak to be understood; listen to understand. Finally, recognize that you may hear something that deserves discretion on your part. Honor that to build trust.
Try peer pressure. I'm not above quietly, professionally, and courteously encouraging other partners to reach out to a problematic partner to have their own conversation. You may find that other partners have their own interests in continuing to work collaboratively, which can serve as an incentive to apply peer pressure, and they often have their own relationship with the problematic one, which may mean the peer-pressure could work. You'll need finesse here to avoid a bull-in-a-china-shop moment, so handle with care.
Despite all your best efforts, sometimes a situation moves beyond sideways and goes upside-down. Even in that situation, you still have alternatives. Are you familiar with the concept of the Best-Alternative-To-Negotiated Agreement (BATNA)? This idea was developed in the series of books, Getting to Yes.
A BATNA isn't the same thing as a fallback position in a negotiation.
A BATNA is needed when a negotiated agreement becomes impossible. Stated another way, you need a fallback position during a negotiation; you need a BATNA when negotiation is no longer possible. A BATNA describes how you might work with your partner when there's no longer a partnership or if they become an adversary. Think about it as the best is you can expect if you or the other parties quit negotiation, leave the mediation, or stop trying to work together collaboratively.
So, in a situation that seems sideways and headed for upside-down, try talking through your BATNA, the best you can expect if you decide to approach the problem in a more confrontational or adversarial way. Ask yourself, "what-if?" There are times when you work with a partner and, despite your best efforts, the dynamics become dysfunctional. It could be that a partner is facing issues you don't even know about, ones they simply can't resolve. In these cases, thinking through a BATNA is helpful.
As part of thinking through a BATNA, I find it helpful to distinguish between working with participants and partners and working with clear adversaries. A thoughtful BATNA will need both elements. What I try to avoid is allowing a small group to hijack everyone else, so a good BATNA should reflect ways the main group can continue to work together and make progress despite acknowledging that there are some folks trying to keep that from happening. What this means is the first part of your BATNA might focus on how to maintain momentum with everyone else in the face of an adversarial challenge from some other quarter.
At the same time, I look for ways to encourage the other folks to reconsider their choice to behave as adversaries, including letting them know they are welcome to rejoin the larger discussion, that it's their choice to do so, and that you hope they'll do so in a less adversarial way.
Knowing that may not work out, I also focus on doing what I can to ensure the process is procedurally sound and that no abuse of administrative discretion has occurred. This is important because the outcome of any future legal challenge generally will hinge on whether a court finds that a procedural violation exists, which usually depends upon whether there is good documentation in the administrative record showing how the agency used its discretion.* Anticipating and finding ways to avoid even the appearance of a procedural violation or of an abuse of agency discretion is good practice regardless, but it's especially important in a sideways or upside-down situation.
At the end of the day, though, remember this: you aren't alone. If you work in partnerships long enough, you will eventually find yourself at odds with a partner. When that happens, reflect on these strategies for getting back on track and, if things seem to be going sideways or upside-down, remember you have these other strategies in your back pocket.
Have more questions or other ideas to add to the mix? Please drop us a line.


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text:
Jim Harbaugh gets his own Topps baseball card
By: Brad Galli
Jim Harbaugh has his face plastered on a lot of football cards.
He now has his own baseball card.
Topps is releasing 2016 Series 2 this week, and features a 'First Pitch' insert subset.
Harbaugh threw out the first pitch at the Tigers game last season, on July 30. He's included in the set wearing a Tigers jersey and hat.
That night, the Michigan head coach wore a maize and blue Tigers hat. For some reason, it looks like Topps photoshopped the Tigers logo on the hat. It is white in the card's picture.

redpajama_set_name: RedPajamaCommonCrawl


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The world's leading manufacturers and research institutions increasingly rely on coordinate measuring machines (CMMs) for component inspection and quality control. Oftentimes, these sophisticated machines must be housed in separate temperature-controlled, contaminant-free environments. Safely and efficiently transporting components to the CMM for inspection can be a problem, especially for large welded assemblies, entire vehicle bodies, etc. An Airfloat CMM Shuttle is ideally suited for such challenges. Download specifications.
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From design and fabrication to assembly and maintenance, Airfloat delivers flexible solutions for CMM transport that are easy and safe to implement. When you want the safest, most ergonomically correct way to move and position heavy loads—regardless of industry—you want Airfloat's air bearing technology.


text:
\section{Introduction}
\blfootnote{*: equal contributions.}
\blfootnote{\dag: work done while at Facebook.}

\input{./tex/introduction}

\section{Related Work}
\input{./tex/related_work}

\section{ImVoteNet Architecture}
\input{./tex/method}

\section{Experiments}
\input{./tex/experiment}

\section{Conclusion}
\input{./tex/conclusion}

{\small
\bibliographystyle{ieee_fullname}

\subsection{Deep Hough Voting}
\textsc{VoteNet}\xspace~\cite{voteNet} is a feed-forward network that consumes a 3D point cloud and outputs object proposals for 3D object detection. Inspired by the seminal work on the generalized Hough transform~\cite{ballard1981generalizing}, \textsc{VoteNet}\xspace proposes an adaptation of the voting mechanism for object detection to a deep learning framework that is fully differentiable.

Specifically, it is comprised of a point cloud feature extraction module that enriches a subsampled set of scene points (called \textit{seeds}) with high-dimensional features (bottom of Fig.~\ref{fig:pipeline} from $N{\times}3$ input points to $K{\times}(3{+}F)$ seeds). These features are then pushed through a Multi-Layer-Perceptron (MLP) to generate \emph{votes}. Every vote is both a point in the 3D space with its Euclidean coordinates (3-dim) supervised to be close to the object center, and a feature vector learned for the final detection task (F-dim). The votes form a clustered point cloud near object centers and are then processed by another point cloud network to generate object proposals and classification scores. This process is equivalent to the pipeline in Fig.~\ref{fig:pipeline} with just the point tower and without the image detection and fusion.

\textsc{VoteNet}\xspace recently achieved state-of-the-art results on indoor 3D object detection in \texttt{RGB-D}\xspace~\cite{voteNet}. Yet, it is solely based on point cloud inputs and neglects the image channels which, as we show in this work, are a very useful source of information. In \textsc{ImVoteNet}\xspace, we leverage the additional image information and propose a lifting module from 2D votes to 3D that improves detection performance. Next, we explain how to get 2D votes in images and how we lift its geometric cues to 3D together with semantic/texture cues.

\subsection{Image Votes from 2D Detection}
\label{sec:image_vote_method}
We generate image votes based on a set of candidate boxes from 2D detectors. An \emph{image vote}, in its geometric part, is simply a vector connecting an image pixel and the center of the 2D object bounding box that pixel belongs to (see Fig.~\ref{fig:teaser}). Each image vote is also augmented with its semantic and texture cues from the features of its source pixel, such that each image vote has $F'$ dimension in total as in the fusion block in Fig.~\ref{fig:pipeline}.

To form the set of boxes given an \texttt{RGB}\xspace image, we apply an off-the-shelf 2D detector (\eg Faster R-CNN~\cite{ren2015faster}) pre-trained on color channels of the \texttt{RGB-D}\xspace dataset. The detector outputs the $M$ most confident bounding boxes and their corresponding classes.
We assign each pixel within a detected box a vote to the box center. Pixels inside multiple boxes are given multiple votes (corresponding 3D seed points are duplicated for each of them), and those outside of any box are padded with zeros. Next we go to details on how we derive geometric, semantic and texture cues.

\paragraph{Geometric cues: lifting image votes to 3D}
The translational 2D votes provide useful geometric cues for 3D object localization. Given the camera matrix, the 2D object center in the image plane becomes a ray in 3D space connecting the 3D object center and the camera optical center (Fig.~\ref{fig:teaser}). Adding this information to a seed point can effectively narrow down the 3D search space of the object center to 1D.

\begin{figure}[t]
    \centering
    \includegraphics[width=1.0\linewidth]{fig/2d-3d-voting-fig3.pdf}
    \caption{\textbf{Illustration of the pseudo 3D vote.} In the figure, $P$ is a surface point in 3D, $C$ is the unknown object center while $p$ and $c$ are their projections on the image plane respectively. $C'$ is the pseudo 3D center and the vector $\protect\vv{PC'}$ is the pseudo 3D vote.}
    \label{fig:2d_to_3d}
\end{figure}

In details, as shown in Fig.~\ref{fig:2d_to_3d}, given an object in 3D with its detected 2D bounding box in the image plane, we denote the 3D object center as $C$ and its projection onto the image as $c$. A point $P$ on the object surface is associated with its projected point $p$ in the image place, hence knowing the 2D vote to the 2D object center $c$, we can reduces the search space for the 3D center to a 1D position on the ray $OC$. We now derive the computation we follow to pass the ray information to the a 3D seed point. Defining $P{=}(x_1, y_1, z_1)$ in the camera coordinate, and $p{=}(u_1, v_1)$, $c{=}(u_2, v_2)$ in the image plane coordinate, we seek to recover the 3D object center $C{=}(x_2, y_2, z_2)$ (voting target for the 3D point $P$).
The true 3D vote from $P$ to $C$ is:

\begin{equation}
    \vv{PC} = (x_2 - x_1, y_2 - y_1, z_2 - z_1).
\end{equation}

The 2D vote, assuming a simple pin-hole camera~\footnote{See supplementary for more details on how to deal with a general camera model and camera-to-world transformations.} with focal length $f$, can be written as:
\begin{equation}
\begin{split}
    \vv{pc} & = (u_2 - u_1, v_2 - v_1) = (\Delta u, \Delta v)\\
    & = (f(\frac{x_2}{z_2} - \frac{x_1}{z_1}), f(\frac{y_2}{z_2} - \frac{y_1}{z_1})).
\end{split}
\end{equation}

We further assume the depth of the surface point $P$ is similar to the center point $C$. This is a reasonable assumption for most objects when they are not too close to the camera.
Then, given $z_1{\approx}z_2$, we compute $\vv{PC'}$,

\begin{equation}
\begin{split}
    \vv{PC'} = (\frac{\Delta u}{f}z_1, \frac{\Delta v}{f}z_1, 0) ,
\end{split}
\end{equation}
which we refer to as a \emph{pseudo 3D vote}, as $C'$ lies on the ray $OC$ and is in the proximity of $C$.
This pseudo 3D vote provides information about where the 3D center is relative to the point surface point $P$.

To compensate for the error caused by the depth approximation ($z_1 \approx z_2$), we pass the ray direction as extra information to the 3D surface point.
The error (along the $X$-axis) caused by the approximated depth, after some derivation, can be expressed by
\begin{equation}
\text{err}_x{=}\Delta x - \Delta x'{=} \frac{x_2}{z_2} (z_2 - z_1).
\end{equation}

Hence, if we input the direction of the ray $\vv{OC}$: $({x_2}/{z_2}, {y_2}/{z_2})$, the network should have more information to estimate the true 3D vote by estimate the depth different $\Delta z = z_2 - z_1$. As we do not know the true 3D object center $C$, we can use the ray direction of $\vv{OC'}$ which aligns with $\vv{OC}$ after all, where
\begin{equation}
\begin{split}
\vv{OC'} & = \vv{OP} + \vv{PC'} \\
& = (x_1 + \frac{\Delta u}{f} z_1, y_1 + \frac{\Delta v}{f} z_1, z_1).
\end{split}
\end{equation}

Normalizing and concatenating with the pseudo vote, the image geometric features we pass to the seed point $P$ are:
\begin{equation}
\label{eq:2dvote}
    (\frac{\Delta u}{f}z_1, \frac{\Delta v}{f}z_1, \frac{\vv{OC'}}{\left\lVert\vv{OC'}\right\rVert}).
\end{equation}

\paragraph{Semantic cues}
On top of the geometric features just discussed that just use the spatial coordinates of the bounding boxes, an important type of information \texttt{RGB}\xspace can provide is features that convey a semantic understanding of what's inside the box. This information often complements what can be learned from 3D point clouds and can help to distinguish between classes that are geometrically very similar (such as table \vs desk or nightstand \vs dresser).

In light of this, we provide additional \emph{region}-level features extracted per bounding box as semantic cues for 3D points. For all the 3D seed points that are projected within the 2D box, we pass a vector representing that box to the point. If a 3D seed point falls into more than one 2D boxes (\ie, when they overlap), we duplicate the seed point for each of the overlapping 2D regions (up to a maximum number of $K$). If a seed point is not projected to any 2D box, we simply pass an all-zero feature vector for padding.

It is important to note that the `region features' here include but are \emph{not} limited to features extracted from \texttt{RoI}\xspace pooling operations~\cite{ren2015faster}. In fact, we find representing each box with a simple one-hot class vector (with a confidence score for that class) is already sufficient to cover the semantic information needed for disambiguation in 3D. It not only gives a light-weight input (\eg 10-dim~\cite{sung2015data} \vs 1024-dim~\cite{lin2017feature}) that performs well, but also generalizes to \emph{all} other competitive (\eg faster) 2D detectors~\cite{redmon2016you,liu2016ssd,lin2017focal} that do not explicitly use \texttt{RoI}\xspace but directly outputs classification scores. Therefore, we use this semantic cue by default.

\paragraph{Texture cues}
Different from the depth information that spreads sparsely in the 3D space, \texttt{RGB}\xspace images can capture high-resolution signals at a dense, per-\emph{pixel} level in 2D. While region features can offer a high-level, semantic-rich representation per bounding box, it is complementary and equally important to use the low-level, texture-rich representations as another type of cues. Such cues can be passed to the 3D seed points via a simple mapping: a seed point gets pixel features from the corresponding pixel of its 2D projection\footnote{If the coordinates after projection is fractional, bi-linear interpolation is used.}.

Although any learned, convolutional feature maps with spatial dimensions (height and width) can serve our purpose, by default we still use the simplest texture feature by feeding in the raw \texttt{RGB}\xspace pixel-values directly. Again, this choice is not only light-weight, but also makes our pipeline \emph{independent} of 2D networks.

Experimentally, we show that even with such minimalist choice of both our semantic and texture cues, significant performance boost over geometric-only \textsc{VoteNet}\xspace can be achieved with our multi-tower training paradigm, which we discuss next.

\begin{table*}[t!]
\centering
\tablestyle{3pt}{1.2}
\begin{tabular}{l|c|x{30}x{30}x{30}x{30}x{30}x{30}x{30}x{30}x{30}x{30}|x{25}}
 methods & \texttt{RGB}\xspace & bathtub & bed & bookshelf & chair & desk & dresser & nightstand & sofa & table & toilet & mAP \\
\shline
DSS~\cite{song2016deep} & \cmark & 44.2 & 78.8 & 11.9 & 61.2 & 20.5 & 6.4 & 15.4 & 53.5 & 50.3 & 78.9 & 42.1    \\
COG~\cite{ren2016three} & \cmark & 58.3 & 63.7 & 31.8 & 62.2 & {\bf 45.2} & 15.5 & 27.4 & 51.0 & {\bf 51.3} & 70.1 & 47.6 \\
2D-driven~\cite{lahoud20172d} & \cmark & 43.5 & 64.5 & 31.4 & 48.3 & 27.9 & 25.9 & 41.9 & 50.4 & 37.0 & 80.4 & 45.1  \\
PointFusion~\cite{xu2018pointfusion} & \cmark & 37.3 & 68.6 & 37.7 & 55.1 & 17.2 & 23.9 & 32.3 & 53.8 & 31.0 & 83.8 & 45.4 \\
F-PointNet~\cite{qi2018frustum} & \cmark & 43.3 & 81.1 & 33.3 & 64.2 & 24.7 & 32.0 & 58.1 & 61.1 & 51.1 & {\bf 90.9} & 54.0 \\
\hshline
\textsc{VoteNet}\xspace~\cite{voteNet} & \xmark & 74.4 & 83.0 & 28.8 & 75.3 & 22.0 & 29.8 & 62.2 & 64.0 & 47.3 & 90.1 & 57.7 \\
\hspace{3mm}+\texttt{RGB}\xspace & \cmark & 70.0 & 82.8 & 27.6 & 73.1 & 23.2 & 27.2 & 60.7 & 63.7 & 48.0 & 86.9 & 56.3 \\
\hspace{3mm}+region feature & \cmark & 71.7 & 86.1 & 34.0 & 74.7 & 26.0 & 34.2 & 64.3 & 66.5 & 49.7 & 88.4 & 59.6 \\
\hshline
\textsc{ImVoteNet}\xspace & \cmark & {\bf 75.9} & {\bf 87.6} & {\bf 41.3} & {\bf 76.7} & 28.7 & {\bf 41.4} & {\bf 69.9} & {\bf 70.7} & 51.1 & 90.5 & {\bf 63.4} \\
\end{tabular}
\vspace{2mm}
\caption{\textbf{3D object detection results on SUN RGB-D v1 val set.} Evaluation metric is average precision with 3D IoU threshold 0.25 as proposed by~\cite{song2015sun}. Note that both COG~\cite{ren2016three} and 2D-driven~\cite{lahoud20172d} use room layout context to boost performance. The evaluation is on the SUN RGB-D v1 data for fair comparisons.
}
\label{tab:sunrgbd}
\vspace{-1mm}
\end{table*}

\subsection{Feature Fusion and Multi-tower Training}
With lifted image votes and its corresponding semantic and texture cues ($K\times F'$ in the fusion block in Fig.~\ref{fig:pipeline}) as well as the point cloud features with the seed points $K \times F$, each seed point can generate 3D votes and aggregate them to propose 3D bounding boxes (through a voting and proposal module similar to that in~\cite{voteNet}).
Yet it takes extra care to optimize the deep network to fully utilize cues from all modalities. As a recent paper~\cite{wang2019makes} mentions, without a careful strategy, multi-modal training can actually result in degraded performance as compared to a single modality training. The reason is that different modalities may learn to solve the task at different rates so, without attention, certain features may dominate the learning and result in over-fitting. In this work, we follow the gradient blending strategy introduced in~\cite{wang2019makes} to weight the gradient for different modality towers (by weighting the loss functions).

In our multi-tower formulation, as shown in Fig.~\ref{fig:pipeline}, we have three towers taking seed points with three sets of features: point cloud features only, image features only and joint features. Each tower has the same target task of detecting 3D objects -- but they each have their separate 3D voting and box proposal network parameters as well as their separate losses. The final training loss is the weighted sum of three detection losses:

\begin{equation}
    L = w_\text{img} L_\text{img} + w_\text{point} L_\text{point} + w_\text{joint} L_\text{joint}.
\end{equation}

Within the image tower, while image features alone cannot localize 3D objects, we have leveraged surface point geometry and camera intrinsic to have pseudo 3D votes that are good approximations to the true 3D votes. So combining this image geometric cue with other semantic/texture cues we can still localize objects in 3D with image features only.

Note that, although the multi-tower structure introduces extra parameters, at inference time we no longer need to compute for the point cloud only and the image only towers -- therefore there is minimal computation overhead.

\section{Overview}
In this supplementary, we provide more details on the \imvotenet architecture in Sec.~\ref{supp:sec:arch}, including point cloud network architecture, 2D detector, 2D votes and image votes lifting. We also show visualizations of the sparse point clouds in Sec.~\ref{supp:sec:visu}.

\section{Details on \imvotenet Architecture}
\label{supp:sec:arch}
In this section, we explain the details in the \imvotenet architecture. Sec.~\ref{supp:sec:pc} provides details in the point cloud deep net as well as the training procedure. Further details on the 2D detector and 2D votes are described in Sec.~\ref{supp:sec:2d} while details on lifting 2D votes with general camera parameters are described in Sec.~\ref{supp:sec:lift}.

\subsection{Point Cloud Network}
\label{supp:sec:pc}
\paragraph{Input and data augmentation.} The point cloud backbone network takes a randomly sampled point cloud of a SUN RGB-D~\cite{song2015sun} depth image with $20k$ points. Each point has its $XYZ$ coordinate as well as its height (distance to floor). The floor height is estimated as the 1\% percentile of heights of the all points.
Similar to~\cite{voteNet}, we augment the input point cloud by randomly sub-sampling the points from the depth image points on-the-fly. Points are also randomly flipped in both horizontal directions and randomly rotated along the up-axis by Uniform[-30,30] degrees. Points are also randomly scaled by Uniform[-.85, 1.15]. Note that the point height and the camera extrinsic are updated accordingly with the augmentation.
\paragraph{Network architecture.} We adopt the same PointNet++~\cite{qi2017pointnetplusplus} backbone network as that in~\cite{voteNet} with four set abstraction (SA) layers and two feature propagation/upsamplng (FP) layers. With input of $N{\times}4$ where $N{=}20k$, the output of the backbone network is a set of seed points of $K{\times}(3+C)$ where $K{=}1024$ and $C{=}256$.

As for voting, different from \textsc{VoteNet}\xspace~ that directly predicts votes from the seed points, here we fuse lifted image votes and the seed points before voting. As each seed point can fall into multiple 2D detection boxes, we duplicate a seed point $q$ times if it falls in $q$ overlapping boxes. Each duplicated seed point has its feature augmented with a concatenation of the following image vote features: 5-dim lifted geometric cues (2 for the vote and 3 for the ray angle), 10-dim (per-class) semantic cues and 3-dim texture cues. In the end the fused seed point has 3-dim $XYZ$ coordinate and a 274-dim feature vector.

The voting layer takes the seed point and maps its features to votes through a multi-layer perceptron (MLP) with FC output sizes of 256, 256 and 259, where the
last FC layer outputs XYZ offset and feature residuals (with regard to the 256-dim seed feature) for the votes.
As in~\cite{voteNet}, the proposal module is another set abstraction layer that takes in the generated votes and generate proposals of shape $K' {\times}(5{+}2NH{+}4NS{+}NC)$ where $K'$ is the number of total duplicated seed points and the output dimension consists of 2 objectness scores, 3 center regression values, $2NH$ numbers for heading regression ($NH$ heading bins) and $4NS$ numbers for box size regression ($NS$ box anchors) and $NC$ numbers for semantic classification.

\paragraph{Training procedure.} We pre-train the 2D detector as described more in Sec.~\ref{supp:sec:2d} and use the extracted image votes as extra input to the point cloud network. We train the point cloud deep net with the Adam optimizer with batch size 8 and an initial learning rate of 0.001. The learning rate is decayed by $10\times$ after 80 epochs and then decayed by another $10\times$ after 120 epochs. Finally, the training stops at 140 epochs as we find further training does not improve performance.

\subsection{2D Detector and 2D Cues}
\label{supp:sec:2d}
\paragraph{2D detector training.} While \imvotenet can work with any 2D detector, in this paper we choose Faster R-CNN~\cite{ren2015faster}, which is the current dominant framework for bounding box detection in \texttt{RGB}\xspace. The detector we used has a basic ResNet-50~\cite{he2016deep} backbone with Feature Pyramid Networks (FPN)~\cite{lin2017feature} constructed as $\{P_2, P_3, \ldots, P_6\}$. It is pre-trained on the COCO \emph{train2017} dataset~\cite{lin2014microsoft} achieving a \emph{val2017} AP of 41.0. To adapt the COCO detector to the specific dataset for 2D detection, we further fine-tune the model using ground truth 2D boxes from the training set of SUN-RGBD before applying the model only using the color channels. The fine-tuning lasts for 4K iterations, with the learning rate reduced by 10${\times\xspace}$ at 3K-th iteration starting from 0.01. The batch size, weight decay, and momentum are set as 8, 1e-4, and 0.9, respectively. Two data augmentation techniques are used: 1) standard left-right flipping; and 2) scale augmentation by randomly sample the shorter side of the input image from [480,600]. The resulting detector achieves a mAP (at overlap 0.5) of 58.5 on val set.

Note that we specifically choose \emph{not} to use the most advanced 2D detectors (\eg based on ResNet-152~\cite{he2016deep}) just for the sake of performance improvement. As our experimental results shown in the main paper, even with this simple baseline Faster R-CNN, we can already see significant boost thanks to the design of \imvotenet.

\paragraph{2D boxes.} To infer 2D boxes using the detector, we first resize the input image to a shorter side of 600 before feeding into the model. Then top 100 detection boxes across all classes for an image is aggregated. We further reduce the number of 2D boxes per image by filtering out any detection with a confidence score below 0.1. Two things to note about the 2D boxes used while training \imvotenet: 1) we could also train with ground truth 2D boxes, however we empirically found that including them for training hurts performance, likely due to the different detection statistics at test time; 2) as the pre-training for the 2D detector is also performed on the same training set, it generally gives better detection results on SUN RGB-D train set images, to reduce the effect of over-fitting, we randomly dropped 2D boxes with a probability of 0.5.

\paragraph{Alternative semantic cues.} Other than the default semantic cue to represent each 2D box region as the one-hot classification score vector (the detected class has the value of the confidence score from the detector, all other locations have zeros), we further experimented with dense \texttt{RoI}\xspace features extracted from that region. Two variants are reported in the paper, with the 1024-dim one being the output from the last FC layer \emph{before} region classification and regression. For the 64-dim one, we insert an additional FC layer before the final output layers so that region information is compressed into the 64-dim vector. The added layer is pre-trained with the 2D detector (resulting in a val mAP of 57.9) and fixed when training \imvotenet.

\paragraph{Alternative texture cues.} The default texture cue is the raw \texttt{RGB}\xspace values (normalized to [-1,1]). Besides this simple texture cue, we also experimented more advanced per-pixel features. One handy feature that preserves such spatial information is the feature maps $P_k$ from FPN that fuse top-down and lateral connections~\cite{lin2017feature}. Here $k$ is the index to the layers in the feature pyramid, which also designates the feature strides and size. For example, $P_2$ has a stride of 2$^2$=4 for both height and width; and a spatial size of roughly 1$/$16 of the input image\footnote{Note that different from 2D box detection, we feed the images directly to the model \emph{without} resizing to shorter-side 600 to compute FPN features.}. For $P_3$ the strides are 2$^3$=8. All feature maps have a channel size of 256, which becomes the input dimension when used as texture cues for \imvotenet.

\subsection{Image Votes Lifting}
\label{supp:sec:lift}

\begin{figure}[t!]
    \centering
    \includegraphics[width=\linewidth]{./fig/lifting_supp}
    \caption{\textbf{Image vote lifting with camera extrinsic.} Here we show surface point $P$ and object center $C$ in two coordinate systems: camera coordinate and upright coordinate ($OY$ is along gravitational direction). $\protect\vv{PC}$ is the true 3D vote. $\protect\vv{PC'}$ is the pseudo vote as calculated in the main paper and $\protect\vv{PC''}$ is the transformed pseudo vote finally used in feature fusion.}
    \label{fig:lift_supp}
\end{figure}

In the main paper we derived the lifting process to transform a 2D image vote to a 3D pseudo vote \emph{without} considering the camera extrinsic. As the point cloud sampled from depth image points is transformed to the \emph{upright coordinate} before feeding to the point cloud network (through camera extrinsic $R$ as a rotational matrix), the 3D pseudo vote also needs to be transformed to the same coordinate.

Fig.~\ref{fig:lift_supp} shows the surface point $P$, object center $C$ and the end point of the pseudo vote $C'$. Since the point cloud is in the upright coordinate, the point cloud deep net can only estimate the depth displacement of $P$ and $C$ along the $Z_{\text{upright}}$ direction (it cannot estimate the depth displacement along the $Z_{\text{camera}}$ direction as the rotational angles from camera to upright coordinate are unknown to the network). Therefore, we need to calculate a new pseudo vote $\vv{PC''}$ where $C''$ is on the ray $OC$ and $PC''$ is perpendicular to the $OZ_{\text{upright}}$.

To calculate the $C''$ we need to firstly transform $P$ and $C'$ to the upright coordinate. Then assume $P{=}(x_p, y_p, z_p)$ and $C'{=}(x_{c'}, y_{c'}, z_{c'})$ in the upright coordinate, we can compute:

\begin{equation}
    C'' = (z_p \frac{x_{c'}}{z_{c'}}, z_p \frac{y_{c'}}{y_{c'}}, z_p).
\end{equation}

\begin{table*}[!t]
\tablestyle{6pt}{1.2}
\begin{tabular}{c|c|c|c|c}
\multicolumn{2}{c|}{point cloud settings} & \multicolumn{3}{c}{images from SUN RGB-D~\cite{song2015sun} train set} \\
\hline
\makecell{sampling\\method} & \# points & 1 & 2 & 3 \\
\shline
\multirow{4}{*}{\makecell{random\\uniform}} & 20k & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005051_vis_random_20000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005052_vis_random_20000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005053_vis_random_20000.jpg}} \\
\cline{2-5}
 & 5k & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005051_vis_random_5000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005052_vis_random_5000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005053_vis_random_5000.jpg}}  \\
 & 1k &  \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005051_vis_random_1000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005052_vis_random_1000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005053_vis_random_1000.jpg}}  \\
\hline
\multirow{2}{*}{ORB~\cite{rublee2011orb}} & 5k & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005051_vis_orb_5000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005052_vis_orb_5000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005053_vis_orb_5000.jpg}}  \\
 & 1k & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005051_vis_orb_1000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005052_vis_orb_1000.jpg}} & \raisebox{-0.015\height}{\includegraphics[width=.25\linewidth]{sparse/005053_vis_orb_1000.jpg}} \\
 \hline
\end{tabular}
\vspace{2mm}
\caption{{\bf Sparse point cloud visualization.} We show projected point clouds on three SUN RGB-D images and compare point density and distribution among random sampling (to $20k$, $5k$ and $1k$ points) and ORB key points based sampling (to $5k$ and $1k$ points). For ORB key point sampling, we firstly detect ORB key points in the RGB images with an ORB key point detector and then keep 3D points that are projected near those key points. Best viewed in color with zoom in. }
\label{tab:sparse}
\vspace{-1mm}
\end{table*}

\section{Visualization of Sparse Points}
\label{supp:sec:visu}
In the Sec. 4.4 of the main paper we showed how image information and \imvotenet model can be specially helpful in detections with sparse point clouds. Here in Table.~\ref{tab:sparse} we visualize the sampled sparse point clouds on three example SUN RGB-D images. We project the sampled points to the RGB images to show their distribution and density. We see that the $20k$ points in the first row have a dense and uniform coverage of the entire scene. After randomly subsampling the points to $5k$ and $1k$ points in the second and third rows respectively, we see the coverage is much more sparse but still uniform. In contrast the ORB key point based sampling (the last two rows) results in very uneven distribution of points where they are clustered around corners and edges. The non-uniformity and low coverage of ORB key points makes it especially difficult to recognize objects in point cloud only. That's also where our \imvotenet model showed the most significant improvement upon \textsc{VoteNet}\xspace.

\subsection{Comparing with State-of-the-art Methods}
\label{sec:exp:sota}
\paragraph{Benchmark dataset.}
\label{sec:exp:benchmark}
We use SUN RGB-D~\cite{silberman2012indoor,janoch2013category,xiao2013sun3d,song2015sun} as our benchmark for evaluation, which is a single-view~\footnote{We do not evaluate on the ScanNet dataset~\cite{dai2017scannet} as in \textsc{VoteNet}\xspace because ScanNet involves \emph{multiple} 2D views for each reconstructed scene -- thus requires extra handling to merge multi-view features.} \texttt{RGB-D}\xspace dataset for 3D scene understanding. It consists of \app10K \texttt{RGB-D}\xspace images, with \app5K for training. Each image is annotated with amodal oriented 3D bounding boxes. In total, 37 object categories are annotated. Following standard evaluation protocol~\cite{song2016deep}, we only train and report results on the 10 most common categories. To feed the data to the point cloud backbone network, we convert the depth images to point clouds using the provided camera parameters. The \texttt{RGB}\xspace image is aligned to the depth channel and is used to query corresponding image regions from scene 3D points.
\paragraph{Methods in comparison.}
\label{sec:exp:method}
We compare \textsc{ImVoteNet}\xspace with previous methods that use both geometry and \texttt{RGB}\xspace. Moreover, since previous state-of-the-art (\textsc{VoteNet}\xspace~\cite{voteNet}) used only geometric information, to better appreciate the improvement due to our proposed fusion and gradient blending modules we add two more strong baselines by extending the basic \textsc{VoteNet}\xspace with additional features from image.

Among the previous methods designed for \texttt{RGB-D}\xspace, 2D-driven~\cite{lahoud20172d}, PointFusion~\cite{xu2018pointfusion} and F-PointNet~\cite{qi2018frustum} are all cascaded systems that rely on 2D detectors to provide proposals for 3D.
Deep Sliding Shapes~\cite{song2016deep} designs a Faster R-CNN~\cite{ren2015faster} style 3D CNN network to generate 3D proposals from voxel input and then combines 3D and 2D \texttt{RoI}\xspace features for box regression and classification. COG~\cite{ren2016three} is a sliding shape based detector using 3D HoG like feature extracted from \texttt{RGB-D}\xspace data.

\begin{figure*}[t!]
    \centering
    \includegraphics[width=.95\linewidth]{fig/votenet2_qual_figure.pdf}
    \caption{\textbf{Qualitative results showing how image information helps.} First row: the bookshelf is detected by \textsc{ImVoteNet}\xspace thanks to the cues from the 2D detector; Second row: the black sofa has barely any depth points due to its material, but leveraging images, we can detect it; Third row: with 2D localization cues and semantics, we detect the desk and chairs in the back which are even missed by ground truth annotations. Best viewed in color with zoom in.}
    \label{fig:qualitative}
\end{figure*}

As for the variations of \textsc{VoteNet}\xspace~\cite{voteNet}, the first one, `+\texttt{RGB}\xspace', directly appends the the \texttt{RGB}\xspace values as a three-dimensional vector to the point cloud features (of the seed points). For the second one (`+region feature'), we use the same pre-trained Faster R-CNN (as in our model) to obtain the region-level one-hot class confidence feature, and concatenate it to the seed points inside that 2D box frustum. These two variations can also be viewed as ablated versions of our method.

\paragraph{Results.}
Table~\ref{tab:sunrgbd} shows the per-class 3D object detection results on SUN RGB-D. We can see that our model outperforms all previous methods by large margins. Especially, it improves upon the previously best model \textsc{VoteNet}\xspace by $\textbf{5.7}$ mAP, showing effectiveness of the lifted 2D image votes. It gets better results on nearly all categories and has the biggest improvements on object categories that are often occluded (+12.5 AP for bookshelves) or geometrically similar to the others (+11.6 AP for dressers and +7.7 AP for nightstands).

Compared to the variations of the \textsc{VoteNet}\xspace that also uses \texttt{RGB}\xspace data, our method also shows significant advantages. Actually we find that naively appending \texttt{RGB}\xspace values to the point features resulted in \emph{worse} performance, likely due to the over-fitting on \texttt{RGB}\xspace values. Adding region features as a one-hot score vector helps a bit but is still inferior compared to our method that more systematically leverage image votes.

\subsection{Qualitative Results and Discussion}
\label{sec:exp:qual}
In Fig.~\ref{fig:qualitative}, we highlight detection results of both the original \textsc{VoteNet}\xspace~\cite{voteNet} (with only point cloud input) and our \textsc{ImVoteNet}\xspace with point cloud plus image input, to show how image information can help 3D detection in various ways. The first example shows how 2D object localization and semantic help. We see a cluttered bookshelf that was missed by the \textsc{VoteNet}\xspace but thanks to the 2D detection in the images, we have enough confidence to recognize it in our network. The image semantics also help our network to avoid the false positive chair as that in the \textsc{VoteNet}\xspace output (coffee table and candles confused the network there). The second example shows how images can compensate depth sensor limitations. Due to the color and material of the black sofa, there is barely any depth point captured for it. While \textsc{VoteNet}\xspace completely misses the sofa, our network is able to pick it up. The third example shows how image cues can push the limit of 3D detection performance, by recovering far away objects (the desk and chairs in the back) that are even missed in the ground truth annotations.

\subsection{Analysis Experiments}
\label{sec:exp:analysis}
In this subsection, we show extensive ablation studies on our design choices and discuss how different modules affect the model performance. For all experiments we report mAP@0.25 on SUN RGB-D as before.

\paragraph{Analysis on geometric cues.} To validate that geometric cues lifted from 2D votes help, we ablate geometric features (as in Eq.~\ref{eq:2dvote}) passed to the 3D seed points in Table~\ref{tab:analysis:geometry}. We see that from row 1 to row 3, not using any 2D geometric cue results in a 2.2 point drop. On the other hand, not using the ray angle resulted in a 1.2 point drop, indicating the ray angle helps provide corrective cue to the pseudo 3D votes.

\begin{table*}
\centering
\subfloat[Ablation studies on 2D \textbf{geometric} cues. 2D vote means the lifted 2D vote (2-dim) as in Eq.~\ref{eq:2dvote} and ray angle means the direction of $\protect\vv{OC'}$ (3-dim). Both geometric cues helped our model.
\label{tab:analysis:geometry}]
{\makebox[0.3\linewidth][c]{
\tablestyle{12pt}{1.2}
\begin{tabular}{c|c|c}
\multicolumn{2}{c|}{geometric cues} & \multirow{2}{*}{mAP} \\
\cline{1-2}
2D vote & ray angle & \\
\shline
\cmark&\cmark & 63.4 \\
 \hline
\cmark& \xmark & 62.2 \\
\xmark& \xmark & 61.2 \\
\multicolumn{3}{c}{}\\
\end{tabular}
}
}
\hfill
\subfloat[Ablation studies on 2D \textbf{semantic} cues. Different region features are experimented. This includes simple one-hot class score vector and rich \texttt{RoI}\xspace features. The former (default) works best.
\label{tab:analysis:semantic}]
{\makebox[0.3\linewidth][c]{
\tablestyle{12pt}{1.2}
\begin{tabular}{c|c|c}
\multicolumn{2}{c|}{semantic cues} & \multirow{2}{*}{mAP} \\
\cline{1-2}
region feature & \# dims  & \\
\shline
one-hot score & 10 & 63.4 \\
\hline
\multirow{2}{*}{\texttt{RoI}\xspace~\cite{ren2015faster}} & 64 & 62.4 \\
& 1024 & 59.5 \\
\hline
\xmark & - & 58.9 \\
\end{tabular}
}
}
\hfill
\subfloat[Ablation studies on 2D \textbf{texture} cues. We experiment with different pixel-level features including \texttt{RGB}\xspace values (default) and learned representations from the feature pyramid.
\label{tab:analysis:pixel}]
{\makebox[0.3\linewidth][c]{
\tablestyle{12pt}{1.2}
\begin{tabular}{c|c|c}
\multicolumn{2}{c|}{texture cues} & \multirow{2}{*}{mAP} \\
\cline{1-2}
pixel feature & \# dims  & \\
\shline
\texttt{RGB}\xspace & 3 & 63.4 \\
\hline
FPN-$P_2$~\cite{lin2017feature} & 256 & 62.0 \\
FPN-$P_3$ & 256 & 62.0 \\
\hline
\xmark & - & 62.4
\end{tabular}
}
}
\vspace{1mm}
\caption{{\bf Ablation analysis} on 2D cues. We provide detailed analysis on all types of features from 2D (see Sec.~\ref{sec:image_vote_method} for detailed descriptions).}
\vspace{-1mm}
\end{table*}

\paragraph{Analysis on semantic cues.}
Table~\ref{tab:analysis:semantic} shows how different types of region features from the 2D images affect 3D detection performance. We see that the one-hot class score vector (probability score for the detected class, other classes set to 0), though simple, leads to the best result. Directly using the 1024-dim \texttt{RoI}\xspace features from the Faster R-CNN network actually got the worst number likely due to the optimization challenge to fuse this high-dim feature with the rest point features. Reducing the 1024-dim feature to 64-dim helps but is still inferior to the simple one-hot score feature.

\paragraph{Analysis on texture cues.} Table~\ref{tab:analysis:pixel} shows how different low-level image features (texture features) affect the end detection performance. It is clear that the raw \texttt{RGB}\xspace features are already effective while the more sophisticated per-pixel CNN features (from feature pyramids~\cite{lin2017feature} of the Faster R-CNN detector) actually hurts probably due to over-fitting. More details are in the supplementary material.

\paragraph{Gradient blending.}
Table~\ref{tab:tower_weights} studies how tower weights affect the gradient blending training. We ablate with a few sets of representative weights ranging from single tower training (the first row), dominating weights for each of the tower (2nd to 4th rows) and our best set up. It is interesting to note that even with just the image features (the 1st row, 4th column) i.e. the pseudo votes and semantic/texture cues from the images, we can already outperform several previous methods (see Table~\ref{tab:sunrgbd}), showing the power of our fusion and voting design.

\begin{table}[t]
\tablestyle{10pt}{1.2}
\begin{tabular}{c|c|c|c|c|c}
\multicolumn{3}{c|}{tower weights} & \multicolumn{3}{c}{mAP} \\
\hline
$w_\text{img}$ & $w_\text{point}$ & $w_\text{joint}$ & image & \makecell{point\\cloud} & joint \\
\shline
- & - & - & \unhighlight{46.8} & \unhighlight{57.4} & \unhighlight{62.1} \\
\hline
0.1 & 0.8 & 0.1 & {\bf 46.9} & 57.8 & 62.7 \\
0.8 & 0.1 & 0.1 & 46.8 & {\bf 58.2} & 63.3 \\
0.1 & 0.1 & 0.8 & 46.1 & 56.8 & 62.7 \\
\hline
0.3 & 0.3 & 0.4 & 46.6 & 57.9 & {\bf 63.4} \\
\end{tabular}
\vspace{2mm}
\caption{Analysis on {\bf multi-tower training}. In the first block we show performance \emph{without} blending in gray. Then we show the setting where each of the tower dominates (0.8) the overall training. Finally we show our default setting where weights are more balanced.}
\label{tab:tower_weights}
\vspace{-1mm}
\end{table}

\subsection{Detection with Sparse Point Clouds}
\label{sec:exp:sparse}
While depth images provide dense point clouds for a scene (usually 10k to 100k points), there are other scenarios that only sparse points are available. One example is when the point cloud is computed through visual odometry~\cite{nister2004visual} or Structure from Motion (SfM)~\cite{koenderink1991affine} where 3D point positions are triangulated by estimating poses of a monocular camera in multiple views. With such sparse data, it is valuable to have a system that can still achieve decent detection performance.

To analyze the potential of our model with sparse point clouds, we simulate scans with much less points through two types of point sub-sampling: uniformly random sub-sampling (remove existing points with a uniform distribution) and ORB~\cite{rublee2011orb} key-point based sub-sampling (sample ORB key points on the image and only keep 3D points that project close to those 2D key points). In Table~\ref{tab:sparse}, we present detection results with different distribution and density of point cloud input. We see that in the column of ``point cloud'', with decreased number of points, 3D detection performance quickly drops. On the other hand, we see including image cues significantly improves performance. This improvement is most significant when the sampled points are from ORB key points that are more non-uniformly distributed.

\begin{table}[t]
\tablestyle{12pt}{1.2}
\begin{tabular}{c|c|c|c|c}
\multicolumn{2}{c|}{point cloud settings} & \multicolumn{3}{c}{mAP} \\
\hline
\makecell{sampling\\method} & \# points & \makecell{point\\cloud} & joint & $\Delta$ \\
\shline
\multirow{3}{*}{\makecell{random\\uniform}} & {20k} & {57.7} & {63.4} & \emph{+5.7} \\
\cline{2-5}
 & 5k & 56.2 & 61.7 & \emph{+5.5} \\
 & 1k & 49.6 & 58.5 & \emph{+8.9} \\
\hline
\multirow{2}{*}{ORB~\cite{rublee2011orb}} & 5k & 32.4 & 49.9 & \emph{+16.5} \\
 & 1k & 27.9 & 47.1 & \emph{+19.2} \\
\end{tabular}
\vspace{2mm}
\caption{{\bf Sparse point cloud} experiment, where we sub-sample the number of points in the cloud either via \emph{random uniform} sampling or with \emph{ORB key points}~\cite{rublee2011orb}. In such cases, our \textsc{ImVoteNet}\xspace significantly outperforms purely geometry based \textsc{VoteNet}\xspace.}
\label{tab:sparse}
\vspace{-1mm}
\end{table}

redpajama_set_name: RedPajamaArXiv


text:
<?php

namespace ShipsUnburned\Model\Entity;

interface MatchInterface
{
    public function getID();

    public function getUser1();

    public function getUser2();

    public function getDate();

    public function getWinner();

    public function getUser1ELO();

    public function getUser2ELO();
}

redpajama_set_name: RedPajamaGithub


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Kotlin is a statically type language. Regarding to that specification, that means that the compiler will know what is the type of a variable or the return type of a function. That's why you can write val str = "hello" instead of val str : String = "hello", or even fun sum(ints: Lst<Int>) = ints.sum() instead of fun sum(ints : List<Int>) : Int = ints.sum(). In a lot of cases you will be allowed to omit the declaration type, so your code should be more concise.
If you do Java, you may know the keyword instanceof that help you defined what is the type of the object you're working on. In Kotlin, we use the keyword is, or !is for the negation.
The is and !is operators help us to avoid type checks along code block. If a variable is checked as a String, the code block that follow that check will consider the variable as a String without casting it. This allow you to call function on variable without be worried of any casting action. It works as well with nullable variable.


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Montgomery Blair High School's Online Student Newspaper —— Thursday, January 23, 2020
Blazers overcome Walter Johnson in first playoff game
Nov. 4, 2009, midnight | By Laura Anthony | 10 years, 2 months ago
Varsity girls's soccer defeats Wildcats 2-0 in first round of playoffs
BLAZER STADIUM, Nov. 3-
The girls's varsity soccer team (11-1) won their first playoff game against the Walter Johnson (WJ) Wildcats 2-0, scoring both goals in the second half after a well-matched struggle at the start of the game.
Sophomore Jamie Kator established Blair's lead about 10 minutes into the second half, with a beautiful shot straight over the goalkeeper's head that soared into the top corner of the net. With only 10 minutes remaining in the game, junior Sofia Read scored an impressive goal after she gained possession immediately following a WJ goalie kick.
soccer11/3 2
Photo: Sophomore Jamie Kator breaks away from the defense and runs upfield in an attempt to score.
Toward the end of the second half, one Wildcats player glided past multiple Blair defenders and came dangerously close to scoring. But Blazer fans were relieved when Blair defense booted the ball down field to Read, although Blair's promising play resulted in nothing more than a futile rush for a goal.
In what Coach Robert Gibb called a "dog fight," the girls held their own in the first half and rose to dominate in the second. The ball roamed on both sides of the field, and both the offense and defense had to work diligently to quell Wildcat threats. The teams seemed evenly matched throughout the scoreless first half, but the Blazers increased the tempo in the second half.
Initially, WJ seemed to step up its intensity at the same rate as the Blazers; they began the second half by thwarting the majority of Blair's attacks. They forced the Blazers to focus on defense in order to combat the Wildcats's creative passing.
But the game seemed beyond them. WJ began to lose what little possession they had in the midfield, gifting Blair with even more chances to score. Kator displayed impressive footwork in the last few minutes of play, but ultimately lost the opportunity for a last-minute goal.
soccer11/3
Photo: Senior Jenna Williams tries to steal the ball.
However, Blair's previous lead was more than enough to ensure them the win.
Both teams had a number of close shots on goal. Within the first two minutes of the second half, the Blair midfield collected possession and fed the ball to the forwards. But the offensive attack ended in disappointment, as Blair was penalized for a foul on the WJ goalkeeper.
Blair had a number of strong offensive drives into scoring territory in the first half, but the team really shined in their defense and goalkeeping. Senior co-captain Imani Pierre was vital in snuffing out every WJ attack, with sophomore goalie Lena Meyerson at hand to stop the few shots they had on target.
Meyerson played extremely well, eliciting applause and sighs of relief from spectators with each skillful save. She easily stopped each Wildcat goal attempt, rushing off her line to stop WJ's shots early.
Gibb said that WJ gave Blair a good game, as was expected. He noted their surprisingly quick offense as one source of trouble for the Blazers. But what mattered in the end was that they still managed to win, Gibb said. The coach's strategy for upcoming playoff games: "Keep winning," he said with a smile.
Blair will continue in the playoffs later this week. Date, time and opponent are TBD.
Laura Anthony. More »


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Lionel Baier (* 13. Dezember 1975 in Lausanne) ist ein Schweizer Filmregisseur und Drehbuchautor sowie Dozent.

Biografie
Die Familie hat väterlicherseits polnische Wurzeln. Er wuchs in Lausanne in einer evangelischen Pfarrersfamilie auf und studierte an der dortigen Universität von 1995 bis 1999 Filmwissenschaft, Französisch und Italienisch. Gemeinsam mit Ursula Meier, Jean-Stéphane Bron und Frédéric Mermoud gründet er 2009 die Produktionsfirma Bande à Part Films mit Sitz in Lausanne.

Seine Filme wurden mehrfach für den Schweizer Filmpreis nominiert bzw. damit ausgezeichnet: Émile de 1 à 5 wurde 2012 in der Kategorie Bester Kurzfilm nominiert. Comme des voleurs war 2007 als Bester Spielfilm nominiert, ebenso Les grandes Ondes (à L'ouest) 2014 sowie 2016 La vanité; die beiden letzten waren ebenfalls für den Preis für das beste Drehbuch nominiert, das Baier gemeinsam mit Julien Boussioux geschrieben hatte. Für ihre Darstellung in La vanité wurden Patrick Lapp mit dem Preis für die beste männliche Hauptrolle sowie Ivan Georgiev mit dem Preis für den besten Nebendarsteller ausgezeichnet.

Seine Filme wurden an diversen namhaften Festivals gezeigt; La vanité wurde 2015 an den Filmfestspielen von Cannes in der Sektion ACID uraufgeführt, und später auf der Piazza Grande am Filmfestival Locarno gezeigt. 2018 erschien Der Fall Mathieu (Ondes de choc – Prénom: Mathieu), als Teil der losen TV-Krimiserie Schockwellen (Ondes de choc), beruhend auf wahren Geschichten. Der Film wurde im Rahmen der Berlinale uraufgeführt. Sein Kinospielfilm La dérive des continents (au sud), mit Isabelle Carré, Théodore Pellerin und Ursina Lardi in den Hauptrollen, feierte 2022 in der Sektion Quinzaine des réalisateurs in Cannes Premiere und wurde ebenfalls am Filmfestival von Locarno aufgeführt.

Nebst seiner Tätigkeit als Regisseur und Drehbuchautor ist er Dozent an der Lausanner Filmhochschule écal, deren Filmdepartement er von 2002 bis 2021 leitete.

Filmografie (Auswahl)

 2000: Celui au pasteur
 2002: La Parade (notre histoire)
 2004: Dummer Junge – Garçon stupide
 2006: Comme des voleurs
 2008: Un autre homme
 2011: La faute à Rousseau (Co-Regie)
 2012: Émile de 1 à 5 (Kurzfilm)
 2013: Les grandes ondes (à l'Ouest)
 2015: La vanité
 2018: Schockwellen – Der Fall Mathieu (Ondes de choc – Prénom: Mathieu)
 2022: La dérive des continents (au sud)

Theater

Inszenierung

 2022 : Foucault en Californie, nach dem Buch von Simeon Wade, Théâtre Vidy-Lausanne

Weblinks

 Interview mit Lionel Baier zum Schreiben mit einem Co-Autoren, von Patrick Claudet, 2006
 Interview mit Lionel Baier zu seinem Film La Vanité, SRF 2016
 Webseite Bande à Part Films

Einzelnachweise

Filmregisseur
Schweizer
Geboren 1975
Mann


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Managing general agents (MGAs) and innovation go well together. A combination of resources, agility, and specialist knowledge sets MGAs apart from other players in insurance, whether carriers, wholesalers, or brokers. An MGA typically has a focus on a sector or an industry and deeper knowledge than a more generalist insurance carrier, facilitating innovation. At the same time, MGAs have underwriting and claims settlement capabilities that brokers and agents do not have, allowing the MGA to push the innovation envelope further. Read more.
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I love technology – how it can help people in their businesses and personal lives – and I enjoy helping people understand the value of its applications.
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