Attention-based Proposals Refinement for 3D Object Detection

• URL: http://arxiv.org/abs/2201.07070v1
• Date: Tue, 18 Jan 2022 15:50:31 GMT
• Title: Attention-based Proposals Refinement for 3D Object Detection
• Authors: Minh-Quan Dao, Elwan H\'ery, Vincent Fr\'emont
• Abstract summary: This paper takes a more data-driven approach to ROI feature extraction using the attention mechanism. Experiments on KITTI textitvalidation set show that our method achieves competitive performance of 84.84 AP for class Car at moderate difficulty.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Safe autonomous driving technology heavily depends on accurate 3D object detection since it produces input to safety critical downstream tasks such as prediction and navigation. Recent advances in this field is made by developing the refinement stage for voxel-based region proposal networks to better strike the balance between accuracy and efficiency. A popular approach among state-of-the-art frameworks is to divide proposals, or Region of Interest (ROI), into grids and extract feature for each grid location before synthesizing them to ROI feature. While achieving impressive performances, such an approach involves a number of hand crafted components (e.g. grid sampling, set abstraction) which requires expert knowledge to be tuned correctly. This paper takes a more data-driven approach to ROI feature extraction using the attention mechanism. Specifically, points inside a ROI are positionally encoded to incorporate ROI 's geometry. The resulted position encoding and their features are transformed into ROI feature via vector attention. Unlike the original multi-head attention, vector attention assign different weights to different channels within a point feature, thus being able to capture a more sophisticated relation between pooled points and ROI. Experiments on KITTI \textit{validation} set show that our method achieves competitive performance of 84.84 AP for class Car at Moderate difficulty while having the least parameters compared to closely related methods and attaining a quasi-real time inference speed at 15 FPS on NVIDIA V100 GPU. The code will be released.

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