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 …
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 …
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 …
Pointr: Diverse point cloud completion with geometry-aware transformers
Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …
Occworld: Learning a 3d occupancy world model for autonomous driving
Understanding how the 3D scene evolves is vital for making decisions in autonomous
driving. Most existing methods achieve this by predicting the movements of object boxes …
driving. Most existing methods achieve this by predicting the movements of object boxes …
Monoscene: Monocular 3d semantic scene completion
AQ Cao, R De Charette - … of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense
geometry and semantics of a scene are inferred from a single monocular RGB image …
geometry and semantics of a scene are inferred from a single monocular RGB image …