Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot
3D voxel-wise segmentation perception problem. These discriminative methods focus on …

Robotic perception requires the modeling of both 3D geometry and semantics. Existing
methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details …

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 …

3D semantic occupancy has garnered considerable attention due to its abundant structural
information encompassing the entire scene in autonomous driving. However, existing 3D …

Holistic understanding and reasoning in 3D scenes play a vital role in the success of
autonomous driving systems. The evolution of 3D semantic occupancy prediction as a …

Understanding how the surrounding environment changes is crucial for performing
downstream tasks safely and reliably in autonomous driving applications. Recent occupancy …

This paper introduces OccFusion, a straightforward and efficient sensor fusion framework for
predicting 3D occupancy. A comprehensive understanding of 3D scenes is crucial in …

This technical report summarizes the winning solution for the 3D Occupancy Prediction
Challenge, which is held in conjunction with the CVPR 2023 Workshop on End-to-End …

3D occupancy prediction based on multi-sensor fusion, crucial for a reliable autonomous
driving system, enables fine-grained understanding of 3D scenes. Previous fusion-based 3D …

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 …