The reconstruction of 3D object from a single image is an important task in the field of
computer vision. In recent years, 3D reconstruction of single image using deep learning …

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 …

To date, various 3D scene understanding tasks still lack practical and generalizable pre-
trained models, primarily due to the intricate nature of 3D scene understanding tasks and …

Humans are able to form a complex mental model of the environment they move in. This
mental model captures geometric and semantic aspects of the scene, describes the …

In this paper, we introduce the task of multi-view RGB-based 3D object detection as an end-
to-end optimization problem. To address this problem, we propose ImVoxelNet, a novel fully …

Abstract Monocular 3D Semantic Scene Completion (SSC) has garnered significant
attention in recent years due to its potential to predict complex semantics and geometry …

The 3D CAD shapes in current 3D benchmarks are mostly collected from online model
repositories. Thus, they typically have insufficient geometric details and less informative …

A comprehensive semantic understanding of a scene is important for many applications-but
in what space should diverse semantic information (eg, objects, scene categories, material …

Semantic reconstruction of indoor scenes refers to both scene understanding and object
reconstruction. Existing works either address one part of this problem or focus on …

Recently, there has been growing interest in developing learning-based methods to detect
and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids …