NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentation

• URL: http://arxiv.org/abs/2602.00542v1
• Date: Sat, 31 Jan 2026 06:16:19 GMT
• Title: NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentation
• Authors: Mohammad Saeid, Amir Salarpour, Pedram MohajerAnsari, Mert D. Pesé,
• Abstract summary: NPNet is a non-parametric approach for 3D point-cloud classification and part segmentation.<n>It builds point features using deterministic operators such as farthest point sampling, k-nearest neighbors, and pooling.<n>It offers favorable memory use and inference time compared to prior non-parametric methods.
• Score: 1.499944454332829
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present NPNet, a fully non-parametric approach for 3D point-cloud classification and part segmentation. NPNet contains no learned weights; instead, it builds point features using deterministic operators such as farthest point sampling, k-nearest neighbors, and pooling. Our key idea is an adaptive Gaussian-Fourier positional encoding whose bandwidth and Gaussian-cosine mixing are chosen from the input geometry, helping the method remain stable across different scales and sampling densities. For segmentation, we additionally incorporate fixed-frequency Fourier features to provide global context alongside the adaptive encoding. Across ModelNet40/ModelNet-R, ScanObjectNN, and ShapeNetPart, NPNet achieves strong performance among non-parametric baselines, and it is particularly effective in few-shot settings on ModelNet40. NPNet also offers favorable memory use and inference time compared to prior non-parametric methods

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