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SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

LiDAR point-cloud segmentation is an important problem for many applications. For large-scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a 2D LiDAR image and use convolutions to process it.

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Cite this as

Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, Masayoshi Tomizuka (2024). Dataset: SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. https://doi.org/10.57702/055z53bg

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Additional Info

Field	Value
Created	December 2, 2024
Last update	December 2, 2024
Defined In	https://doi.org/10.48550/arXiv.2004.01803
Author	Chenfeng Xu
More Authors	Bichen Wu Zining Wang Wei Zhan Peter Vajda Kurt Keutzer Masayoshi Tomizuka
Homepage	https://github.com/chenfengxu714/SqueezeSegV3