We study the challenging problem of localizing and classifying category-specific object contours in real world images. For this purpose, we present a simple yet effective method ...
Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev...
We present a method for fusing two acquisition modes, 2D photographs and 3D LiDAR scans, for depth-layer decomposition of urban facades. The two modes have complementary character...
Yangyan Li, Qian Zheng, Andrei Sharf, Daniel Cohen...
Predicting human occupations in photos has great application potentials in intelligent services and systems. However, using traditional classification methods cannot reliably dis...
We propose a novel method for computing a geometrically consistent and spatially dense matching between two 3D shapes. Rather than mapping points to points we match infinitesimal...
Thomas Windheuser, Ulrich Schlickewei, Frank R. Sc...
Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can...
In this paper, we propose an efficient technique to detect changes in the geometry of an urban environment using some images observing its current state. The proposed method can ...
We present a method for detecting 3D objects using multi-modalities. While it is generic, we demonstrate it on the combination of an image and a dense depth map which give complem...
Stefan Hinterstoisser, Stefan Holzer, Cedric Cagni...
In this paper, we present a novel, threshold-free robust estimation framework capable of efficiently fitting models to contaminated data. While RANSAC and its many variants have...
We present a generalized subgraph preconditioning (GSP) technique to solve large-scale bundle adjustment problems efficiently. In contrast with previous work which uses either di...
We propose a novel framework for imposing label ordering constraints in multilabel optimization. In particular, label jumps can be penalized differently depending on the jump dire...