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

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 …

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 …

Abstract 3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal
undertaking in autonomous driving aiming to predict the voxel occupancy within volumetric …

Abstract 3D occupancy prediction is an important task for the robustness of vision-centric
autonomous driving which aims to predict whether each point is occupied in the surrounding …

Abstract During the Gaussian Splatting optimization process, the scene geometry can
gradually deteriorate if its structure is not deliberately preserved, especially in non-textured …

Semantic scene completion (SSC) aims to jointly estimate the complete geometry and
semantics of a scene, assuming partial sparse input. In the last years following the …

We propose a novel approach aimed at object and semantic scene completion from a partial
scan represented as a 3D point cloud. Our architecture relies on three novel layers that are …

Point clouds are often the default choice for many applications as they exhibit more flexibility
and efficiency than volumetric data. Nevertheless, their unorganized nature–points are …

The goal of the Semantic Scene Completion (SSC) task is to simultaneously predict a
completed 3D voxel representation of volumetric occupancy and semantic labels of objects …