• DOI:10.1007/978-3-030-58604-1_1
Corpus ID: 214802232

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

@inproceedings{Xu2020SqueezeSegV3SC,
  title={SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation},
  author={Chenfeng Xu and B. Wu and Z. Wang and Wei Zhan and P. Vajda and K. Keutzer and M. Tomizuka},
  booktitle={ECCV},
  year={2020}
}

Chenfeng Xu, B. Wu, +4 authors M. Tomizuka

Published in ECCV 2020

Computer Science

LiDAR point-cloud segmentation is an important problem for many applications. For large-scale point cloud segmentation, the \textit{de facto} method is to project a 3D point cloud to get a 2D LiDAR image and use convolutions to process it. Despite the similarity between regular RGB and LiDAR images, we discover that the feature distribution of LiDAR images changes drastically at different image locations. Using standard convolutions to process such LiDAR images is problematic, as convolution… CONTINUE READING

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