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arXiv · 2506.23478

GeoCD: A Differential Local Approximation for Geodesic Chamfer Distance

Pedro Alonso·Tianrui Li·Chongshou Li
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Abstract

Chamfer Distance (CD) is a widely adopted metric in 3D point cloud learning due to its simplicity and efficiency. However, it suffers from a fundamental limitation: it relies solely on Euclidean distances, which often fail to capture the intrinsic geometry of 3D shapes. To address this limitation, we propose GeoCD, a topology-aware and fully differentiable approximation of geodesic distance designed to serve as a metric for 3D point cloud learning. Our experiments show that GeoCD consistently improves reconstruction quality over standard CD across various architectures and datasets. We demonstrate this by fine-tuning several models, initially trained with standard CD, using GeoCD. Remarkably, fine-tuning for a single epoch with GeoCD yields significant gains across multiple evaluation metrics.

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Pedro Alonso, Tianrui Li, Chongshou Li. 2025-06-30. GeoCD: A Differential Local Approximation for Geodesic Chamfer Distance. https://arxiv.org/abs/2506.23478

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