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

MT-Net Submission to the Waymo 3D Detection Leaderboard