mlsanityData to 14 Oct 2026

SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields

arXiv 2408.06697 ↗First posted 13 Aug 2024cs.CV cs.AI cs.LGcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 56 of its 64 references that are in the map. First posted on arXiv 13 Aug 2024.

Builds on

56 of 64 references.

References · 56

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 5.

12 papers.