Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware Approach
Partial shape matching is a crucial yet underexplored problem in 3D vision, with significant relevance to real-world scenarios where shapes are often only partially observed.
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Key points
- While existing approaches have employed DINO features for partial shape matching, these features are not inherently suited for handling partial observations.
- In this work, we propose a method to refine DINO features using LoRA-based self-supervised learning, enabling the generation of feature descriptors that are robust to partiality.
- Our features substantially improve performance on partial shape matching compared to traditional or vision foundation features.
- Additionally, when integrated into existing partial shape matching pipelines, we achieve state-of-the-art results on partial shape matching and left-right prediction benchmarks.
Sources (1)
- [1]Teaching DINOv3 About Partial 3D Geometry: A Self-Supervised Geometry-Aware ApproacharXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 12:51 PM
Partial shape matching is a crucial yet underexplored problem in 3D vision, with significant relevance to real-world scenarios where shapes are often only partially observed.
While existing approaches have employed DINO features for partial shape matching, these features are not inherently suited for handling partial observations.
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