Change detection is an important step for the characterization of object dynamics at the
earth's surface. In multi-temporal point clouds, the main challenge is to detect true changes …

Training deep models for semantic scene completion is challenging due to the sparse and
incomplete input, a large quantity of objects of diverse scales as well as the inherent label …

The computer vision, graphics, and machine learning research groups have given a
significant amount of focus to 3D object recognition (segmentation, detection, and …

Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in
the entire 3D scene from limited observations, which is an emerging and critical task for …

Semantic scene completion (SSC) jointly predicts the semantics and geometry of the entire
3D scene, which plays an essential role in 3D scene understanding for autonomous driving …

3D Semantic Scene Completion (SSC) has emerged as a novel task in vision-based holistic
3D scene understanding. Its objective is to densely predict the occupancy and category of …

At present the perception system of autonomous vehicles is grounded on 3D vision
technologies along with deep learning to process depth information. Although deep learning …

Point cloud completion is the task of producing a complete 3D shape given an input of a
partial point cloud. It has become a vital process in 3D computer graphics, vision and …

3D semantic occupancy prediction is a pivotal task in the field of autonomous driving.
Recent approaches have made great advances in 3D semantic occupancy predictions on a …

This paper presented DriveArena, the first high-fidelity closed-loop simulation system
designed for driving agents navigating in real scenarios. DriveArena features a flexible …