Papers › PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas
Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds and well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.
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Code
Syntology Ran 89 of 164 code samples harvested from 44 repositories linked to this paper; 75 have no recorded run. Of those that ran: 9 ran · our draft was wrong; 14 ran · fixture could not drive it; 66 ran with no contract checked.
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110 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
164 samples harvested; 89 ran; 0 honoured the contract we drafted; 75 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
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