Comprehensive review of deep learning-based 3d point cloud completion processing and analysis
Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
Tri-perspective view for vision-based 3d semantic occupancy prediction
Modern methods for vision-centric autonomous driving perception widely adopt the bird's-
eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than …
eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than …
Voxformer: Sparse voxel transformer for camera-based 3d semantic scene completion
Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This
appealing ability is vital for recognition and understanding. To enable such capability in AI …
appealing ability is vital for recognition and understanding. To enable such capability in AI …
Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving
Robotic perception requires the modeling of both 3D geometry and semantics. Existing
methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details …
methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details …
Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving
Abstract 3D scene understanding plays a vital role in vision-based autonomous driving.
While most existing methods focus on 3D object detection, they have difficulty describing …
While most existing methods focus on 3D object detection, they have difficulty describing …
Grid-centric traffic scenario perception for autonomous driving: A comprehensive review
Grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, grid-centric perception is less prevalent than object-centric perception as …
Nonetheless, grid-centric perception is less prevalent than object-centric perception as …
Rethinking range view representation for lidar segmentation
LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …
or voxel-based methods as they often yield better performance than the traditional range …
Spherical transformer for lidar-based 3d recognition
LiDAR-based 3D point cloud recognition has benefited various applications. Without
specially considering the LiDAR point distribution, most current methods suffer from …
specially considering the LiDAR point distribution, most current methods suffer from …
Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception
Semantic occupancy perception is essential for autonomous driving, as automated vehicles
require a fine-grained perception of the 3D urban structures. However, existing relevant …
require a fine-grained perception of the 3D urban structures. However, existing relevant …
Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction
The vision-based perception for autonomous driving has undergone a transformation from
the bird-eye-view (BEV) representations to the 3D semantic occupancy. Compared with the …
the bird-eye-view (BEV) representations to the 3D semantic occupancy. Compared with the …