Self-Attention Based Context-Aware 3D Object Detection
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Most existing point-cloud based 3D object detectors use convolution-like operators to process information in a local neighbourhood with fixed-weight kernels and aggregate global context hierarchically. However, recent work on non-local neural networks and self-attention for 2D vision has shown that explicitly modeling global context and long-range interactions between positions can lead to more robust and competitive models. In this paper, we explore two variants of self-attention for contextual modeling in 3D object detection by augmenting convolutional features with self-attention features. We first incorporate the pairwise self-attention mechanism into the current state-of-the-art BEV, voxel and point-based detectors and show consistent improvement over strong baseline models while simultaneously significantly reducing their parameter footprint and computational cost. We also propose a self-attention variant that samples a subset of the most representative features by learning deformations over randomly sampled locations. This not only allows us to scale explicit global contextual modeling to larger point-clouds, but also leads to more discriminative and informative feature descriptors. Our method can be flexibly applied to most state-of-the-art detectors with increased accuracy and parameter and compute efficiency. We achieve new state-of-the-art detection performance on KITTI and nuScenes datasets. Code is available at <https://github.com/AutoVision-cloud/SA-Det3D>.
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