Papers › ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum
The performance of neural networks scales with both their size and the amount of data they have been trained on. This is shown in both language and image generation. However, this requires scaling-friendly network architectures as well as large-scale datasets. Even though scaling-friendly architectures like transformers have emerged for 3D vision tasks, the GPT-moment of 3D vision remains distant due to the lack of training data. In this paper, we introduce ARKit LabelMaker, the first large-scale, real-world 3D dataset with dense semantic annotations. Specifically, we complement ARKitScenes dataset with dense semantic annotations that are automatically generated at scale. To this end, we extend LabelMaker, a recent automatic annotation pipeline, to serve the needs of large-scale pre-training. This involves extending the pipeline with cutting-edge segmentation models as well as making it robust to the challenges of large-scale processing. Further, we push forward the state-of-the-art performance on ScanNet and ScanNet200 dataset with prevalent 3D semantic segmentation models, demonstrating the efficacy of our generated dataset.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Semantic Segmentation | ScanNet200 | PTv3 ArKitLabelmaker | test mIoU | 41.4 | #3 of 16 | Archive leaderboard | report |
| 3D Semantic Segmentation | ScanNet200 | PTv3 ArKitLabelmaker | val mIoU | 40.3 | #3 of 16 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | PTv3 ARKit LabelMaker | test mIoU | 79.8 | #3 of 45 | Archive leaderboard | report |
| Semantic Segmentation | ScanNet | PTv3 ARKit LabelMaker | val mIoU | 79.1 | #3 of 45 | 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.
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