An End-to-End Transformer Model for 3D Object Detection

Ishan Misra, Rohit Girdhar, Armand Joulin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 2906-2917

Abstract


We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds. Compared to existing detection methods that employ a number of 3D-specific inductive biases, 3DETR requires minimal modifications to the vanilla Transformer block. Specifically, we find that a standard Transformer with non-parametric queries and Fourier positional embeddings is competitive with specialized architectures that employ libraries of 3D-specific operators with hand-tuned hyperparameters. Nevertheless, 3DETR is conceptually simple and easy to implement, enabling further improvements by incorporating 3D domain knowledge. Through extensive experiments, we show 3DETR outperforms the well-established and highly optimized VoteNet baselines on the challenging ScanNetV2 dataset by 9.5%. Furthermore, we show 3DETR is applicable to 3D tasks beyond detection, and can serve as a building block for future research.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Misra_2021_ICCV, author = {Misra, Ishan and Girdhar, Rohit and Joulin, Armand}, title = {An End-to-End Transformer Model for 3D Object Detection}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {2906-2917} }