13 Result(s)
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Chapter and Conference Paper
Single Image Intrinsic Decomposition Without a Single Intrinsic Image
Intrinsic image decomposition—decomposing a natural image into a set of images corresponding to different physical causes—is one of the key and fundamental problems of computer vision. Previous intrinsic decom...
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Chapter and Conference Paper
Deep Continuous Fusion for Multi-sensor 3D Object Detection
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that...
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Chapter and Conference Paper
End-to-End Deep Structured Models for Drawing Crosswalks
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple LiDAR sweeps and the corresponding imagery, we project both inputs onto the ground ...
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Chapter and Conference Paper
Exploiting Semantic Information and Deep Matching for Optical Flow
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well a...
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Chapter and Conference Paper
HouseCraft: Building Houses from Rental Ads and Street Views
In this paper, we utilize rental ads to create realistic textured 3D models of building exteriors. In particular, we exploit the address of the property and its floorplan, which are typically available in the ...
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Chapter and Conference Paper
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation
In this paper we propose a slanted plane model for jointly recovering an image segmentation, a dense depth estimate as well as boundary labels (such as occlusion boundaries) from a static scene given two frame...
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Chapter and Conference Paper
Efficient Exact Inference for 3D Indoor Scene Understanding
In this paper we propose the first exact solution to the problem of estimating the 3D room layout from a single image. This problem is typically formulated as inference in a Markov random field, where potentia...
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Chapter and Conference Paper
Continuous Markov Random Fields for Robust Stereo Estimation
In this paper we present a novel slanted-plane model which reasons jointly about occlusion boundaries as well as depth. We formulate the problem as one of inference in a hybrid MRF composed of both continuous ...
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Chapter and Conference Paper
Efficient Large-Scale Stereo Matching
In this paper we propose a novel approach to binocular stereo for fast matching of high-resolution images. Our approach builds a prior on the disparities by forming a triangulation on a set of support points w...
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Chapter and Conference Paper
Learning to Recognize Objects from Unseen Modalities
In this paper we investigate the problem of exploiting multiple sources of information for object recognition tasks when additional modalities that are not present in the labeled training set are available for...
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Chapter and Conference Paper
Patch-Based Pose Inference with a Mixture of Density Estimators
This paper presents a patch-based approach for pose estimation from single images using a kernelized density voting scheme. We introduce a boosting-like algorithm that models the density using a mixture of wei...
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Chapter and Conference Paper
3D Human Body Tracking Using Deterministic Temporal Motion Models
There has been much effort invested in increasing the robustness of human body tracking by incorporating motion models. Most approaches are probabilistic in nature and seek to avoid becoming trapped into local...
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Chapter and Conference Paper
Hierarchical Implicit Surface Joint Limits to Constrain Video-Based Motion Capture
To increase the reliability of existing human motion tracking algorithms, we propose a method for imposing limits on the underlying hierarchical joint structures in a way that is true to life. Unlike most exis...
