Papers › 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser
Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://3dmatch.cs.princeton.edu
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Reconstruction | Scan2CAD | 3DMatch | Average Accuracy | 10.29% | #2 of 2 | Archive leaderboard | report |
| Point Cloud Registration | 3DMatch Benchmark | 3DMatch + RANSAC | Feature Matching Recall | 66.8 | #13 of 15 | Archive leaderboard | report |
| Point Cloud Registration | ETH (trained on 3DMatch) | 3DMatch | Feature Matching Recall | 0.169 | #10 of 20 | Archive leaderboard | report |
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