SlotLifter: スロット誘導特徴リフティングによるオブジェクト中心放射輝度場の学習
SlotLifter: Slot-guided Feature Lifting for Learning Object-centric Radiance Fields
スロット誘導特徴リフティングを用いて、3Dシーンの再構成とオブジェクト分解を同時に行うオブジェクト中心の放射輝度モデルを提案し、合成・実世界データセットで高い性能を示した。
著者: Yu Liu, Baoxiong Jia, Yixin Chen, Siyuan Huang
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
