SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields
<|reference_start|>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.<|reference_end|>
@article{liu2024slotlifter:,
    title={SlotLifter: Slot-guided Feature Lifting for Learning Object-centric
  Radiance Fields},
    author={Yu Liu, Baoxiong Jia, Yixin Chen, Siyuan Huang},
    journal={arXiv preprint arXiv:2408.06697},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06697},
    primaryClass={cs.CV cs.AI cs.LG cs.RO}
}