  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.
