PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via Multi-Modal Distribution Estimation

• URL: http://arxiv.org/abs/2304.04884v2
• Date: Wed, 09 Apr 2025 11:21:48 GMT
• Title: PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via Multi-Modal Distribution Estimation
• Authors: Jie Zhang, Minghui Nie, Changqing Zou, Jian Liu, Ligang Liu, Junjie Cao,
• Abstract summary: PointNorm-Net is the first self-supervised deep learning framework to tackle this challenge.<n>Our method achieves superior generalization and outperforms state-of-the-art conventional and deep learning approaches across three real-world datasets.
• Score: 29.582507073730913
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although supervised deep normal estimators have recently shown impressive results on synthetic benchmarks, their performance deteriorates significantly in real-world scenarios due to the domain gap between synthetic and real data. Building high-quality real training data to boost those supervised methods is not trivial because point-wise annotation of normals for varying-scale real-world 3D scenes is a tedious and expensive task. This paper introduces PointNorm-Net, the first self-supervised deep learning framework to tackle this challenge. The key novelty of PointNorm-Net is a three-stage multi-modal normal distribution estimation paradigm that can be integrated into either deep or traditional optimization-based normal estimation frameworks. Extensive experiments show that our method achieves superior generalization and outperforms state-of-the-art conventional and deep learning approaches across three real-world datasets that exhibit distinct characteristics compared to the synthetic training data.

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