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.

Data and Resources

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

DOI retrieved: December 2, 2024

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