Papers › Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts
Ji Hou, Benjamin Graham, Matthias Nießner, Saining Xie
The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. point clouds) are notoriously hard. For example, the number of scenes (e.g. indoor rooms) that can be accessed and scanned might be limited; even given sufficient data, acquiring 3D labels (e.g. instance masks) requires intensive human labor. In this paper, we explore data-efficient learning for 3D point cloud. As a first step towards this direction, we propose Contrastive Scene Contexts, a 3D pre-training method that makes use of both point-level correspondences and spatial contexts in a scene. Our method achieves state-of-the-art results on a suite of benchmarks where training data or labels are scarce. Our study reveals that exhaustive labelling of 3D point clouds might be unnecessary; and remarkably, on ScanNet, even using 0.1% of point labels, we still achieve 89% (instance segmentation) and 96% (semantic segmentation) of the baseline performance that uses full annotations.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Semantic Segmentation | ScanNet200 | CSC | test mIoU | 24.9 | #15 of 16 | Archive leaderboard | report |
| 3D Semantic Segmentation | ScanNet200 | CSC | val mIoU | 26.4 | #15 of 16 | Archive leaderboard | report |
| Semantic Segmentation | S3DIS Area5 | CSC+MinkUNet | mIoU | 72.2 | #23 of 61 | Archive leaderboard | report |
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