VoxAfford: オープンボキャブラリ3Dアフォーダンス検出のためのマルチスケールボクセルトークン融合
VoxAfford: Multi-Scale Voxel-Token Fusion for Open-Vocabulary 3D Affordance Detection
3D点群上のオープンボキャブラリアフォーダンス検出において、MLLMの出力トークンに事前学習済み3D VQVAEのマルチスケール幾何特徴を融合する手法を提案し、mIoUを約8%向上させた。
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2. 先行研究と比べてどこがすごい？
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著者: Haowen Sun, Shaolong Zhang, Mingyang Li, Chengzhong Ma, Xinzhe Chen, Qiongjie Cui, Xingyu Chen, Zeyang Liu, Xuguang Lan
分類: cs.CV, cs.RO
原文アブストラクト
Open-vocabulary 3D affordance detection requires localizing interaction regions on point clouds given novel affordance descriptions. Recent methods extend multimodal large language models (MLLMs) with special output tokens that are decoded into segmentation masks. However, these tokens are produced through autoregressive generation, which models sequential dependencies rather than spatial neighborhood relations, leaving them semantically rich but spatially impoverished for 3D localization. We propose Voxel-enhanced Affordance detection (VoxAfford), which bypasses this bottleneck by injecting multi-scale geometric features from a frozen pre-trained 3D VQVAE encoder into the output tokens after generation. Each output token uses its affordance semantics as a query to retrieve relevant geometric patterns from its paired voxel scale via cross-attention, with a learned compatibility gate controlling the injection strength. The enhanced tokens are then aggregated into a spatially-aware affordance prompt through semantic-conditioned attention and propagated alongside per-point features to generate the final mask. Experiments on open-vocabulary affordance detection tasks show that VoxAfford achieves state-of-the-art performance with approximately an 8% improvement in mIoU, and real robot experiments confirm zero-shot transfer to novel objects.
