Develop a method of annotating 3d sparse data (point cloud) in an efficient way with the help of deep neural network models and user corrections. Take the approach of human-in-the-loop to refine a AI generated fine annotation of the data. Focus on the task of self-driving cars and lidar sensor observations. The model generates a denser representation of the data and refines it by leveraging interactive human 2d annotations.Outgoin
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
Develop a method of annotating 3d sparse data (point cloud) in an efficient way with the help of dee...
3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can gener...
Recent advances in Light Detection and Ranging (LiDAR) sensors have led to an increasing amount of l...
3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can gener...
Currently, 3D point clouds are being used widely due to their reliability in presenting 3D objects a...
A LiDAR point cloud is 3D data which contains millions of data points represented in the form I (x, ...
Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and c...
Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generall...
CVPR 2023International audienceWe propose a new self-supervised method for pre-training the backbone...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated tr...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
Develop a method of annotating 3d sparse data (point cloud) in an efficient way with the help of dee...
3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can gener...
Recent advances in Light Detection and Ranging (LiDAR) sensors have led to an increasing amount of l...
3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can gener...
Currently, 3D point clouds are being used widely due to their reliability in presenting 3D objects a...
A LiDAR point cloud is 3D data which contains millions of data points represented in the form I (x, ...
Accurate semantic segmentation of 3D point clouds is a long-standing problem in remote sensing and c...
Autonomous vehicles perceive objects through various sensors. Cameras, radar, and LiDAR are generall...
CVPR 2023International audienceWe propose a new self-supervised method for pre-training the backbone...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
Accurate semantic segmentation of unstructured 3D point clouds requires large amount of annotated tr...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...
International audienceLiDAR point clouds are receiving a growing interest in remote sensing as they ...