Deep Learning based Joint Geometry and Attribute Up-sampling for Large-Scale Colored Point Clouds

• URL: http://arxiv.org/abs/2506.22749v1
• Date: Sat, 28 Jun 2025 04:08:44 GMT
• Title: Deep Learning based Joint Geometry and Attribute Up-sampling for Large-Scale Colored Point Clouds
• Authors: Yun Zhang, Feifan Chen, Na Li, Zhiwei Guo, Xu Wang, Fen Miao, Sam Kwong,
• Abstract summary: We propose a deep learning-based Joint Geometry and Attribute Up-sampling (JGAU) method to generate large-scale colored point clouds.<n>We release a large-scale dataset for colored point cloud up-sampling called SYSU-PCUD.<n>Experiments show that the Peak Signal-to-Noise Ratio (PSNR) achieved by the proposed JGAU method is 33.90 decibels.
• Score: 46.83969238599941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Colored point cloud, which includes geometry and attribute components, is a mainstream representation enabling realistic and immersive 3D applications. To generate large-scale and denser colored point clouds, we propose a deep learning-based Joint Geometry and Attribute Up-sampling (JGAU) method that learns to model both geometry and attribute patterns while leveraging spatial attribute correlations. First, we establish and release a large-scale dataset for colored point cloud up-sampling called SYSU-PCUD, containing 121 large-scale colored point clouds with diverse geometry and attribute complexities across six categories and four sampling rates. Second, to improve the quality of up-sampled point clouds, we propose a deep learning-based JGAU framework that jointly up-samples geometry and attributes. It consists of a geometry up-sampling network and an attribute up-sampling network, where the latter leverages the up-sampled auxiliary geometry to model neighborhood correlations of the attributes. Third, we propose two coarse attribute up-sampling methods, Geometric Distance Weighted Attribute Interpolation (GDWAI) and Deep Learning-based Attribute Interpolation (DLAI), to generate coarse up-sampled attributes for each point. Then, an attribute enhancement module is introduced to refine these up-sampled attributes and produce high-quality point clouds by further exploiting intrinsic attribute and geometry patterns. Extensive experiments show that the Peak Signal-to-Noise Ratio (PSNR) achieved by the proposed JGAU method is 33.90 decibels, 32.10 decibels, 31.10 decibels, and 30.39 decibels for up-sampling rates of 4 times, 8 times, 12 times, and 16 times, respectively. Compared to state-of-the-art methods, JGAU achieves average PSNR gains of 2.32 decibels, 2.47 decibels, 2.28 decibels, and 2.11 decibels at these four up-sampling rates, demonstrating significant improvement.

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