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Paper detail

MT-Net Submission to the Waymo 3D Detection Leaderboard

In this technical report, we introduce our submission to the Waymo 3D Detection leaderboard. Our network is based on the Centerpoint architecture, but with significant improvements. We design a 2D backbone to utilize multi-scale features for better detecting objects with various sizes, together with an optimal transport-based target assignment strategy, which dynamically assigns richer supervision signals to the detection candidates. We also apply test-time augmentation and model-ensemble for further improvements. Our submission currently ranks 4th place with 78.45 mAPH on the Waymo 3D Detection leaderboard.

preprint2022arXivOpen access
Shaoxiang ChenZequn JieXiaolin WeiLin Ma
Computer Vision
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Shaoxiang ChenZequn JieXiaolin WeiLin Ma

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