APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction

• URL: http://arxiv.org/abs/2509.08104v1
• Date: Tue, 09 Sep 2025 19:31:06 GMT
• Title: APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction
• Authors: Sasan Sharifipour, Constantino Álvarez Casado, Mohammad Sabokrou, Miguel Bordallo López,
• Abstract summary: Training deep learning models for point cloud prediction tasks depends critically on loss functions that measure discrepancies between predicted and ground-truth point sets.<n>We propose Adaptive Probabilistic Matching Loss (APML), a fully differentiable approximation of one-to-one matching.<n>We analytically compute the temperature to guarantee a minimum probability, eliminating manual tuning.
• Score: 16.82777427285544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training deep learning models for point cloud prediction tasks such as shape completion and generation depends critically on loss functions that measure discrepancies between predicted and ground-truth point sets. Commonly used functions such as Chamfer Distance (CD), HyperCD, and InfoCD rely on nearest-neighbor assignments, which often induce many-to-one correspondences, leading to point congestion in dense regions and poor coverage in sparse regions. These losses also involve non-differentiable operations due to index selection, which may affect gradient-based optimization. Earth Mover Distance (EMD) enforces one-to-one correspondences and captures structural similarity more effectively, but its cubic computational complexity limits its practical use. We propose the Adaptive Probabilistic Matching Loss (APML), a fully differentiable approximation of one-to-one matching that leverages Sinkhorn iterations on a temperature-scaled similarity matrix derived from pairwise distances. We analytically compute the temperature to guarantee a minimum assignment probability, eliminating manual tuning. APML achieves near-quadratic runtime, comparable to Chamfer-based losses, and avoids non-differentiable operations. When integrated into state-of-the-art architectures (PoinTr, PCN, FoldingNet) on ShapeNet benchmarks and on a spatiotemporal Transformer (CSI2PC) that generates 3D human point clouds from WiFi CSI measurements, APM loss yields faster convergence, superior spatial distribution, especially in low-density regions, and improved or on-par quantitative performance without additional hyperparameter search. The code is available at: https://github.com/apm-loss/apml.

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