International audienceWe propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich representation of contextual relationships between object parts, which is then exploited by a graph convolutional network. Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset)
Recent advances in Light Detection and Ranging (LiDAR) sensors have led to an increasing amount of l...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have...
Machine learning has made phenomenal progress in the past decades. This work has a focus on the chal...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand t...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand t...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand t...
Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral w...
Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral w...
Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and c...
Recent advances in Light Detection and Ranging (LiDAR) sensors have led to an increasing amount of l...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
International audienceWe propose a novel deep learning-based framework to tackle the challenge of se...
Semantic segmentation of point clouds is indispensable for 3D scene understanding. Point clouds have...
Machine learning has made phenomenal progress in the past decades. This work has a focus on the chal...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand t...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand t...
Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand t...
Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral w...
Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral w...
Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and c...
Recent advances in Light Detection and Ranging (LiDAR) sensors have led to an increasing amount of l...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...
We describe an effective and efficient method for point-wise semantic classification of 3D point clo...