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arXiv · 2408.06697

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

Yu Liu·Baoxiong Jia·Yixin Chen·Siyuan Huang
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Abstract

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.

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Yu Liu, Baoxiong Jia, Yixin Chen, Siyuan Huang. 2024-08-13. SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields. https://arxiv.org/abs/2408.06697

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