mlsanityData to 7 Oct 2026

MT-Net Submission to the Waymo 3D Detection Leaderboard

arXiv 2207.04781 ↗First posted 11 Jul 2022cs.CVcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 7 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

5 of 7 references.

References · 5

  1. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  2. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  3. 2017Submanifold Sparse Convolutional Networkscited by 223
  4. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 177
  5. 2021Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awarenesscited by 4

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2023LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusioncited by 80
  2. 2023DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Cloudscited by 31
  3. 2023Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detectioncited by 28
  4. 2024AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformercited by 1
  5. 2024Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVscited by 0