DINOcular: 自己教師あり視空間表現の学習
DINOcular: Self-Supervised Visuospatial Representations
RGB-D観測から視覚と空間情報を同時に学習する自己教師ありフレームワークを提案し、3D認識性能を向上させつつセマンティック転移も維持する。
著者: Farkhat Almukhamedov, Sami Azirar, Hermann Blum
分類: cs.CV
原文アブストラクト
We introduce a self-supervised framework for learning joint visuospatial representations from RGB-D observations. While modern vision foundation models are trained almost exclusively on RGB images, many embodied systems have access to explicit depth sensing, which provides geometric information that monocular inputs cannot recover. Our method integrates depth-derived geometric priors with a visual backbone through inter-patch and intra-patch fusion, enabling the model to encode both appearance and spatial structure efficiently. The resulting representation shows promising improvements on 3D awareness while preserving semantic transfer: it outperforms prior methods of comparable scale on multiple 3D geometry benchmarks, and remains competitive when probed for standard RGB-D semantic segmentation tasks.