We are interested in computer vision and machine learning with a focus on 3D scene understanding, parsing, reconstruction, material and motion estimation for autonomous intelligent systems such as self-driving cars or household robots. In particular, we investigate how complex prior knowledge can be incorporated into computer vision algorithms for making them robust to variations in our complex 3D world. You can follow us on GoogleScholar (paper email alert) and on YouTube (video email alert).
While ground truth datasets spur innovation, many current datasets for evaluating stereo, optical flow, scene flow and other tasks are restricted in terms of size, complexity, and diversity, making it difficult to train and test on realistic data. For example, we co-authored the Middlebury flow dataset \cite{Baker:IJCV:11... Read More
While the accuracy of optical flow estimation has increased markedly, a number of problems remain, most notably the treatment of motion and image boundaries, the tracking of fast but small/thin objects, and the computational complexity of current methods. In \cite{Sun:IJCV:2014}, we comprehensively analyze ... Read More
While many computer vision problems are formulated as purely bottom-up processes, it is well known that top-down cues play an important role in human perception. But how can we integrate this high-level knowledge into current models? In this project, we investigate this question and propose models for stereo \cite{Guney2015CVPR}, sc... Read More
Holistic scene understanding is an important prerequisite for many indoor and outdoor applications, including autonomous driving, navigation, indoor and outdoor mapping as well as localization. Given a high-dimension input (e.g., image or video stream), the task is to extract a rich but compact representation that is easily acc... Read More
Abstract We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; ... Read More
Existing learning based solutions to 3D surface prediction cannot be trained end-to-end as they operate on intermediate representations (e.g., TSDF) from which 3D surface meshes must be extracted in a post-processing step (e.g., via the marching cubes algorithm). In this paper, we investigate the problem of end-to-end 3D surface pre... Read More
We propose a novel model and dataset for 3D scene flow estimation with an application to autonomous driving. Taking advantage of the fact that outdoor scenes often decompose into a small number of independently moving objects, we represent each element in the scene by its rigid motion parameters and... Read More
In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) allow learning the entire task from data. However, they do not incorporate the physics of image formation such as perspective geometry and occlusion. Instea... Read More
Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this work, we therefore prop... Read More
Best Paper Award at 3DV 2015! This paper presents a novel probabilistic foundation for volumetric 3-d reconstruction. We formulate the problem as inference in a Markov random field, which accurately captures the dependencies between the occupancy and appearance of each voxel, given all in... Read More
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