An End-to-End Transformer Model for 3D Object Detection
This addresses the problem of 3D object detection for robotics and autonomous systems, offering a simpler alternative to specialized architectures.
The paper tackles 3D object detection in point clouds by proposing 3DETR, an end-to-end Transformer model that achieves a 9.5% improvement over VoteNet on the ScanNetV2 dataset.
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.