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Status: Unchecked ×Keyword: im2col ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

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

   Xu, Wu, Wang et al. · arXiv (Cornell University) · 2020

   The paper proposes Spatially-Adaptive Convolution for LiDAR images, because standard convolutions suit them poorly, and uses it to build SqueezeSegV3 for point-cloud segmentation.

   Unchecked3 claims
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   1. UncheckedA network built with spatially-adaptive convolution, SqueezeSegV3, scored at least 3.7% mIoU higher than earlier published methods on SemanticKITTI at similar speed.“Using SAC, we build SqueezeSegV3 for LiDAR point-cloud segmentation and outperform all previous published methods by at least 3.7% mIoU on the SemanticKITTI benchmark with comparable inference speed.”
   2. UncheckedThe paper says Spatially-Adaptive Convolution can be computed efficiently, because it reduces to element-wise multiplications, im2col and a standard convolution.“SAC can be computed efficiently since it can be implemented as a series of element-wise multiplications, im2col, and standard convolution.”
   3. UncheckedThe authors present Spatially-Adaptive Convolution (SAC) as a general framework, with several earlier methods being special cases of it.“It is a general framework such that several previous methods can be seen as special cases of SAC.”