Scene Understanding

Scene Understanding

Project Overview

We are working on algorithms for understanding real-world scenes from visual input. Our work has yielded the widely used dense random field model and, more recently, new convolutional network architectures. We have also developed approaches to estimating the spatial layout of scenes.


Dilated Residual Networks

Playing for Data: Ground Truth from Computer Games

Feature Space Optimization for Semantic Video Segmentation

Dense Monocular Depth Estimation in Complex Dynamic Scenes

Multi-Scale Context Aggregation by Dilated Convolutions

Parameter Learning and Convergent Inference for Dense Random Fields

Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials