In recent years, convolutional neural networks (CNNs) are leading the way in many
computer vision tasks, such as image classification, object detection, and face recognition. In …

Depth estimation and 3D object detection are critical for scene understanding but remain
challenging to perform with a single image due to the loss of 3D information during image …

In this paper, we consider the problem of recovering the spatial layout of indoor scenes from
monocular images. The presence of clutter is a major problem for existing single-view 3D …

The field-of-view of standard cameras is very small, which is one of the main reasons that
contextual information is not as useful as it should be for object detection. To overcome this …

Since most current scene understanding approaches operate either on the 2D image or
using a surface-based representation, they do not allow reasoning about the physical …

We present a human-centric paradigm for scene understanding. Our approach goes beyond
estimating 3D scene geometry and predicts the" workspace" of a human which is …

There has been a recent push in extraction of 3D spatial layout of scenes. However, none of
these approaches model the 3D interaction between objects and the spatial layout. In this …

In this paper we show that a geometric representation of an object occurring in indoor
scenes, along with rich scene structure can be used to produce a detector for that object in a …

What primitives should we use to infer the rich 3D world behind an image? We argue that
these primitives should be both visually discriminative and geometrically informative and we …

We propose a method for understanding the 3D geometry of indoor environments (eg
bedrooms, kitchens) while simultaneously identifying objects in the scene (eg beds …