title: SlotLifter: Slot-guided Feature Lifting for Learning Object-centric
    Radiance Fields
summary: 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.
paper_authors._id: 66bc2903205bee54789ef386
paper_authors.name: Yu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T11:00:36.984Z
paper_authors._id: 636de85cc4a7a729c164d2b5
paper_authors.avatarUrl: /avatars/3e281e547e1697e1c06805e7e63f3918.svg
paper_authors.fullname: Yu Liu
paper_authors.type: user
paper_authors.user: YuLiu
paper_authors._id: 66bc2903205bee54789ef387
paper_authors.name: Baoxiong Jia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T11:00:45.565Z
paper_authors._id: 6304b389bad6ce7fc02691d5
paper_authors.avatarUrl: /avatars/a762ca59624ce409650165f36b973488.svg
paper_authors.fullname: Baoxiong Jia
paper_authors.type: user
paper_authors.user: BuzzBeater
paper_authors._id: 66bc2903205bee54789ef388
paper_authors.name: Yixin Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T11:01:18.568Z
paper_authors._id: 64c396def6fe448b1ad553d6
paper_authors.avatarUrl: /avatars/b2ce4739f42dc00ee974fff7ee1cb301.svg
paper_authors.fullname: Yixin Chen
paper_authors.type: user
paper_authors.user: YixinChen
paper_authors._id: 66bc2903205bee54789ef389
paper_authors.name: Siyuan Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T11:01:35.037Z
paper_authors._id: 63c7a33121bd95f80ed74652
paper_authors.avatarUrl: /avatars/7dd59afea785a2bff0ec2b757abd474e.svg
paper_authors.fullname: Siyuan Huang
paper_authors.type: user
paper_authors.user: thuhsy
paper_title: SlotLifter: Slot-guided Feature Lifting for Learning Object-centric
    Radiance Fields
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: SlotLifter object-centric radiance model scene reconstruction decomposition slot-guided feature lifting image-based rendering scene decomposition novel-view synthesis