ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
Authors: name: Guangda Ji, name: Silvan Weder, name: Francis Engelmann, name: Marc Pollefeys, name: 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.