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arXiv · 2407.15282

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

Xiaoyang Wu·Xiang Xu·Lingdong Kong·Liang Pan·Ziwei Liu·Tong He·Wanli Ouyang·Hengshuang Zhao
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Abstract

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.

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Xiaoyang Wu, Xiang Xu, Lingdong Kong, Liang Pan, Ziwei Liu, Tong He, Wanli Ouyang, Hengshuang Zhao. 2024-07-21. Point Transformer V3 Extreme: 1st Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation. https://arxiv.org/abs/2407.15282

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