mlsanityData to 14 Oct 2026

Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation

arXiv 2407.15282 ↗First posted 21 Jul 2024cs.CVcited by 0

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Abstract (arXiv)

In this technical report, we detail our first-place solution for the 2024 Waymo Open Dataset Challenge's semantic segmentation track. We significantly enhanced the performance of Point Transformer V3 on the Waymo benchmark by implementing cutting-edge, plug-and-play training and inference technologies. Notably, our advanced version, Point Transformer V3 Extreme, leverages multi-frame training and a no-clipping-point policy, achieving substantial gains over the original PTv3 performance. Additionally, employing a straightforward model ensemble strategy further boosted our results. This approach secured us the top position on the Waymo Open Dataset semantic segmentation leaderboard, markedly outperforming other entries.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 21 references that are in the map. First posted on arXiv 21 Jul 2024.

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19 of 21 references.

References · 19

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  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  4. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
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  6. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
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  9. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  10. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  11. 2021Conditional Positional Encodings for Vision Transformerscited by 406
  12. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
7 more
  1. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  2. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 109
  3. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  4. 2023Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Trainingcited by 86
  5. 2023FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformercited by 73
  6. 2023Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learningcited by 56
  7. 2024OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentationcited by 44

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