We study open-world 3D scene understanding, a family of tasks that require agents to
reason about their 3D environment with an open-set vocabulary and out-of-domain visual …

This paper focuses on semantic scene completion, a task for producing a complete 3D voxel
representation of volumetric occupancy and semantic labels for a scene from a single-view …

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 …

We introduce an approach that leverages surface normal predictions, along with
appearance cues, to retrieve 3D models for objects depicted in 2D still images from a large …

In this paper, we propose a learning-based approach to the task of automatically extracting
a" wireframe" representation for images of cluttered man-made environments. The wireframe …

Given a single photo of a room and a large database of furniture CAD models, our goal is to
reconstruct a scene that is as similar as possible to the scene depicted in the photograph …

Abstract We introduce Spatial Group Convolution (SGC) for accelerating the computation of
3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial …

Semantic scene completion predicts volumetric occupancy and object category of a 3D
scene, which helps intelligent agents to understand and interact with the surroundings. In …

Abstract Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric
occupancy and semantic category of a 3D scene. It helps intelligent devices to understand …

The task of estimating the spatial layout of cluttered indoor scenes from a single RGB image
is addressed in this work. Existing solutions to this problem largely rely on hand-crafted …