Geometry Meets Deep Learning Workshop, ICCV 2019International audienceFusion of 2D images and 3D point clouds is important because information from dense images can enhance sparse point clouds. However, fusion is challenging because 2D and 3D data live in different spaces. In this work, we propose MVPNet (Multi-View PointNet), where we aggregate 2D multi-view image features into 3D point clouds, and then use a point based network to fuse the features in 3D canonical space to predict 3D semantic labels. To this end, we introduce view selection along with a 2D-3D feature aggregation module. Extensive experiments show the benefit of leveraging features from dense images and reveal superior robustness to varying point cloud density compared to ...
Semantic segmentation of 3D point cloud is an essential task for autonomous driving environment perc...
Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to a...
Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to a...
Geometry Meets Deep Learning Workshop, ICCV 2019International audienceFusion of 2D images and 3D poi...
Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clo...
Many point cloud segmentation methods rely on transferring irregular points into a voxel-based regul...
In this paper, we propose a novel joint instance and semantic segmentation approach, which is called...
Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. P...
Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. P...
Automation in point cloud data processing is central in knowledge discovery within decision-making s...
In this paper, we address the problem of reconstructing an object’s surface from a single image usin...
Three-dimensional (3D) point cloud semantic segmentation is fundamental in complex scene perception....
Deep learning has achieved tremendous progress and success in processing images and natural language...
In this paper, we propose a Structure-Aware Fusion Network (SAFNet) for 3D scene understanding. As 2...
In spite of the good performance of convolutional neural network (CNN) and graph neural network (GNN...
Semantic segmentation of 3D point cloud is an essential task for autonomous driving environment perc...
Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to a...
Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to a...
Geometry Meets Deep Learning Workshop, ICCV 2019International audienceFusion of 2D images and 3D poi...
Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clo...
Many point cloud segmentation methods rely on transferring irregular points into a voxel-based regul...
In this paper, we propose a novel joint instance and semantic segmentation approach, which is called...
Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. P...
Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. P...
Automation in point cloud data processing is central in knowledge discovery within decision-making s...
In this paper, we address the problem of reconstructing an object’s surface from a single image usin...
Three-dimensional (3D) point cloud semantic segmentation is fundamental in complex scene perception....
Deep learning has achieved tremendous progress and success in processing images and natural language...
In this paper, we propose a Structure-Aware Fusion Network (SAFNet) for 3D scene understanding. As 2...
In spite of the good performance of convolutional neural network (CNN) and graph neural network (GNN...
Semantic segmentation of 3D point cloud is an essential task for autonomous driving environment perc...
Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to a...
Semantic segmentation is a key approach to comprehensive image data analysis. It can be applied to a...