SlotLifter: Slot-guided Feature Lifting for Learning Object-centric
  Radiance Fields
The ability to distill object-centric abstractions from intricate visual
scenes underpins human-level generalization. Despite the significant progress
in object-centric learning methods, learning object-centric representations in
the 3D physical world remains a crucial challenge. In this work, we propose
SlotLifter, a novel object-centric radiance model addressing scene
reconstruction and decomposition jointly via slot-guided feature lifting. Such
a design unites object-centric learning representations and image-based
rendering methods, offering state-of-the-art performance in scene decomposition
and novel-view synthesis on four challenging synthetic and four complex
real-world datasets, outperforming existing 3D object-centric learning methods
by a large margin. Through extensive ablative studies, we showcase the efficacy
of designs in SlotLifter, revealing key insights for potential future
directions.
66bc2903205bee54789ef386
Yu Liu
admin_assigned
2024-08-14T11:00:36.984Z
636de85cc4a7a729c164d2b5
/avatars/3e281e547e1697e1c06805e7e63f3918.svg
Yu Liu
user
YuLiu
66bc2903205bee54789ef387
Baoxiong Jia
admin_assigned
2024-08-14T11:00:45.565Z
6304b389bad6ce7fc02691d5
/avatars/a762ca59624ce409650165f36b973488.svg
Baoxiong Jia
user
BuzzBeater
66bc2903205bee54789ef388
Yixin Chen
admin_assigned
2024-08-14T11:01:18.568Z
64c396def6fe448b1ad553d6
/avatars/b2ce4739f42dc00ee974fff7ee1cb301.svg
Yixin Chen
user
YixinChen
66bc2903205bee54789ef389
Siyuan Huang
admin_assigned
2024-08-14T11:01:35.037Z
63c7a33121bd95f80ed74652
/avatars/7dd59afea785a2bff0ec2b757abd474e.svg
Siyuan Huang
user
thuhsy
SlotLifter: Slot-guided Feature Lifting for Learning Object-centric
  Radiance Fields
The ability to distill object-centric abstractions from intricate visual
scenes underpins human-level generalization. Despite the significant progress
in object-centric learning methods, learning object-centric representations in
the 3D physical world remains a crucial challenge. In this work, we propose
SlotLifter, a novel object-centric radiance model addressing scene
reconstruction and decomposition jointly via slot-guided feature lifting. Such
a design unites object-centric learning representations and image-based
rendering methods, offering state-of-the-art performance in scene decomposition
and novel-view synthesis on four challenging synthetic and four complex
real-world datasets, outperforming existing 3D object-centric learning methods
by a large margin. Through extensive ablative studies, we showcase the efficacy
of designs in SlotLifter, revealing key insights for potential future
directions.
SlotLifter, a novel object-centric radiance model, achieves state-of-the-art performance in scene decomposition and novel-view synthesis by integrating object-centric learning with image-based rendering techniques.
SlotLifter object-centric radiance model scene reconstruction decomposition slot-guided feature lifting image-based rendering scene decomposition novel-view synthesis