Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC),
presents a significant challenge in computer vision. Previous methods, confined to onboard …

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 …

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 …

In this paper we present a tensor decomposition and low-rank recovery approach
(LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods …

Monocular Semantic Occupancy Prediction aims to infer the complete 3D geometry and
semantic information of scenes from only 2D images. It has garnered significant attention …

Human driver can easily describe the complex traffic scene by visual system. Such an ability
of precise perception is essential for driver's planning. To achieve this, a geometry-aware …

Semantic occupancy perception is essential for autonomous driving, as automated vehicles
require a fine-grained perception of the 3D urban structures. However, existing relevant …

We study outdoor 3D scene understanding, a challenging task demanding the intelligent
system to infer both geometry and semantics from a single-view image–a critical skill for …

The autonomous driving community has shown significant interest in 3D occupancy
prediction driven by its exceptional geometric perception and general object recognition …

Semantic scene completion also known as semantic occupancy prediction can provide
dense geometric and semantic information for autonomous vehicles which attracts the …