SlotLifter: Slot-guided Feature Lifting for Learning Object-centric
  Radiance Fields
Authors: name: Yu Liu, name: Baoxiong Jia, name: Yixin Chen, name: Siyuan Huang

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.