Papers › Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
Kevin Tirta Wijaya, Dong-Hee Paek, Seung-Hyun Kong
Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud. Unfortunately, the compounded loss of information concerning granularity and non-maximum point features due to sampling and max pooling could adversely affect the high-semantic point features from existing networks such that they are insufficient to represent the local context of a point cloud, which in turn may hinder the network in distinguishing fine shapes. To cope with this problem, we propose a novel point cloud feature learning network, PointStack, using multi-resolution feature learning and learnable pooling (LP). The multi-resolution feature learning is realized by aggregating point features of various resolutions in the multiple layers, so that the final point features contain both high-semantic and high-resolution information. On the other hand, the LP is used as a generalized pooling function that calculates the weighted sum of multi-resolution point features through the attention mechanism with learnable queries, in order to extract all possible information from all available point features. Consequently, PointStack is capable of extracting high-semantic point features with minimal loss of information concerning granularity and non-maximum point features. Therefore, the final aggregated point features can effectively represent both global and local contexts of a point cloud. In addition, both the global structure and the local shape details of a point cloud can be well comprehended by the network head, which enables PointStack to advance the state-of-the-art of feature learning on point clouds. The codes are available at https://github.com/kaist-avelab/PointStack.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Point Cloud Classification | ModelNet40 | PointStack | Mean Accuracy | 89.6 | #67 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ModelNet40 | PointStack | Overall Accuracy | 93.3 | #67 of 111 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | PointStack | Mean Accuracy | 86.2 | #44 of 77 | Archive leaderboard | report |
| 3D Point Cloud Classification | ScanObjectNN | PointStack | Overall Accuracy | 87.2 | #44 of 77 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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