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GRNet: Gridding Residual Network for Dense Point Cloud Completion
European Conference on Computer Vision, 2020Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer Perceptrons (MLPs) to directly process point clouds, which may cause the ...
Haozhe Xie +5 more
semanticscholar +1 more source
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
European Conference on Computer Vision, 2020LiDAR 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.
Chenfeng Xu +6 more
semanticscholar +1 more source