Indoor environment model reconstruction has emerged as a significant and challenging task
in terms of the provision of a semantically rich and geometrically accurate indoor model …

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 …

Convolutional and adversarial networks are found in various fields of knowledge and
activities. One such field is building design, a multi-disciplinary and multi-task process …

This paper presents a self-supervised framework for training interest point detectors and
descriptors suitable for a large number of multiple-view geometry problems in computer …

Deep multitask networks, in which one neural network produces multiple predictive outputs,
can offer better speed and performance than their single-task counterparts but are …

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

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 …

We propose an algorithm to predict room layout from a single image that generalizes across
panoramas and perspective images, cuboid layouts and more general layouts (eg" L"-shape …

3D layout is crucial for scene understanding and reconstruction, and very useful in
applications like real estate and furniture design. In this paper, we propose a fully automatic …

This paper proposes a deep neural network (DNN) for piece-wise planar depthmap
reconstruction from a single RGB image. While DNNs have brought remarkable progress to …