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The overall architecture of our implementation of 3D object detection consists of three main components: (a) Feature extraction module. (b) BEV fusion encoder module. The input radar and image features are encoded mainly through local image sampling attention and maximum adjacent radar sampling attention to generate BEV features. (c) Object decoder module. Content encoding is initialized using heat maps, and position encoding is generated using dynamic reference points. In addition, noisy queries are added to the query to help stabilize the matching process.
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In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly c...
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