Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches
arXiv:2312.08230v2 Announce Type: replace-cross Abstract: Detecting partial extrinsic symmetry in 3D geometry is a fundamental yet persistent challenge in computer vision and graphics, critical for tasks ranging from shape completion to procedural generation. Classical transformation-space voting methods rely on pairwise matching, scaling as O(n^2) and struggling to resolve coherent multi-instance groups. Recent learning approaches advance global symmetry detection but restrict the solution space to reflection planes, failing to capture rotational or translational repetitions such as the legs
The paper leverages contrastive learning, a recent advancement in AI, to tackle a long-standing challenge in computer vision for 3D geometry, indicating ongoing progress in fundamental AI capabilities.
Improved 3D symmetry detection is crucial for enhancing the efficiency and capabilities of applications in robotics, design automation, and virtual reality, impacting multiple industrial sectors.
The proposed method could lead to more efficient and robust detection of complex partial symmetries in 3D objects, potentially accelerating development in areas requiring detailed geometric understanding beyond simple reflections.
- · Computer Vision Researchers
- · Robotics Companies
- · 3D Animation & Design Software Developers
- · Advanced Manufacturing
- · Traditional O(n^2) symmetry detection methods
More accurate and faster processing of complex 3D models becomes achievable.
This could lead to substantial improvements in the procedural generation of highly detailed digital assets and physical components.
The enhanced 3D spatial understanding could contribute to more adaptive and versatile robotic manipulation in unstructured environments.
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Read at arXiv cs.LG