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23 results (View BibTeX file of all listed publications)

2015

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Exploiting Object Similarity in 3D Reconstruction

Zhou, C., Güney, F., Wang, Y., Geiger, A.

In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

Abstract
Despite recent progress, reconstructing outdoor scenes in 3D from movable platforms remains a highly difficult endeavor. Challenges include low frame rates, occlusions, large distortions and difficult lighting conditions. In this paper, we leverage the fact that the larger the reconstructed area, the more likely objects of similar type and shape will occur in the scene. This is particularly true for outdoor scenes where buildings and vehicles often suffer from missing texture or reflections, but share similarity in 3D shape. We take advantage of this shape similarity by locating objects using detectors and jointly reconstructing them while learning a volumetric model of their shape. This allows us to reduce noise while completing missing surfaces as objects of similar shape benefit from all observations for the respective category. We evaluate our approach with respect to LIDAR ground truth on a novel challenging suburban dataset and show its advantages over the state-of-the-art.

pdf suppmat [BibTex]

2015

Zhou, C., Güney, F., Wang, Y., Geiger, A. Exploiting Object Similarity in 3D Reconstruction In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

pdf suppmat [BibTex]

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FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation

Lenz, P., Geiger, A., Urtasun, R.

In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

Abstract
One of the most popular approaches to multi-target tracking is tracking-by-detection. Current min-cost flow algorithms which solve the data association problem optimally have three main drawbacks: they are computationally expensive, they assume that the whole video is given as a batch, and they scale badly in memory and computation with the length of the video sequence. In this paper, we address each of these issues, resulting in a computationally and memory-bounded solution. First, we introduce a dynamic version of the successive shortest-path algorithm which solves the data association problem optimally while reusing computation, resulting in faster inference than standard solvers. Second, we address the optimal solution to the data association problem when dealing with an incoming stream of data (i.e., online setting). Finally, we present our main contribution which is an approximate online solution with bounded memory and computation which is capable of handling videos of arbitrary length while performing tracking in real time. We demonstrate the effectiveness of our algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art performance, while being significantly faster than existing solvers.

pdf suppmat video project [BibTex]

Lenz, P., Geiger, A., Urtasun, R. FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

pdf suppmat video project [BibTex]

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Intrinsic Depth: Improving Depth Transfer with Intrinsic Images

Kong, N., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3514-3522, December 2015 (inproceedings)

Abstract
We formulate the estimation of dense depth maps from video sequences as a problem of intrinsic image estimation. Our approach synergistically integrates the estimation of multiple intrinsic images including depth, albedo, shading, optical flow, and surface contours. We build upon an example-based framework for depth estimation that uses label transfer from a database of RGB and depth pairs. We combine this with a method that extracts consistent albedo and shading from video. In contrast to raw RGB values, albedo and shading provide a richer, more physical, foundation for depth transfer. Additionally we train a new contour detector to predict surface boundaries from albedo, shading, and pixel values and use this to improve the estimation of depth boundaries. We also integrate sparse structure from motion with our method to improve the metric accuracy of the estimated depth maps. We evaluate our Intrinsic Depth method quantitatively by estimating depth from videos in the NYU RGB-D and SUN3D datasets. We find that combining the estimation of multiple intrinsic images improves depth estimation relative to the baseline method.

pdf suppmat YouTube official video poster Project Page Project Page [BibTex]

Kong, N., Black, M. J. Intrinsic Depth: Improving Depth Transfer with Intrinsic Images In IEEE International Conference on Computer Vision (ICCV), pages: 3514-3522, December 2015 (inproceedings)

pdf suppmat YouTube official video poster Project Page Project Page [BibTex]

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Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences

Bogo, F., Black, M. J., Loper, M., Romero, J.

In International Conference on Computer Vision (ICCV), pages: 2300-2308, December 2015 (inproceedings)

Abstract
We accurately estimate the 3D geometry and appearance of the human body from a monocular RGB-D sequence of a user moving freely in front of the sensor. Range data in each frame is first brought into alignment with a multi-resolution 3D body model in a coarse-to-fine process. The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation. Our novel body model has variable shape detail, allowing it to capture faces with a high-resolution deformable head model and body shape with lower-resolution. Finally we combine range data from an entire sequence to estimate a high-resolution displacement map that captures fine shape details. We compare our recovered models with high-resolution scans from a professional system and with avatars created by a commercial product. We extract accurate 3D avatars from challenging motion sequences and even capture soft tissue dynamics.

Video pdf Project Page Project Page [BibTex]

Bogo, F., Black, M. J., Loper, M., Romero, J. Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences In International Conference on Computer Vision (ICCV), pages: 2300-2308, December 2015 (inproceedings)

Video pdf Project Page Project Page [BibTex]

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3D Object Reconstruction from Hand-Object Interactions

Tzionas, D., Gall, J.

In International Conference on Computer Vision (ICCV), pages: 729-737, December 2015 (inproceedings)

Abstract
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.

pdf Project's Website Video Spotlight Extended Abstract YouTube DOI Project Page [BibTex]

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Proceedings of the 37th German Conference on Pattern Recognition

Gall, J., Gehler, P., Leibe, B.

Springer, German Conference on Pattern Recognition, October 2015 (proceedings)

GCPR conference website [BibTex]

Gall, J., Gehler, P., Leibe, B. Proceedings of the 37th German Conference on Pattern Recognition Springer, German Conference on Pattern Recognition, October 2015 (proceedings)

GCPR conference website [BibTex]

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Towards Probabilistic Volumetric Reconstruction using Ray Potentials

(Best Paper Award)

Ulusoy, A. O., Geiger, A., Black, M. J.

In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)

Abstract
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 input images. Our main contribution is an approximate highly parallelized discrete-continuous inference algorithm to compute the marginal distributions of each voxel's occupancy and appearance. In contrast to the MAP solution, marginals encode the underlying uncertainty and ambiguity in the reconstruction. Moreover, the proposed algorithm allows for a Bayes optimal prediction with respect to a natural reconstruction loss. We compare our method to two state-of-the-art volumetric reconstruction algorithms on three challenging aerial datasets with LIDAR ground truth. Our experiments demonstrate that the proposed algorithm compares favorably in terms of reconstruction accuracy and the ability to expose reconstruction uncertainty.

code YouTube pdf suppmat DOI Project Page [BibTex]

Ulusoy, A. O., Geiger, A., Black, M. J. Towards Probabilistic Volumetric Reconstruction using Ray Potentials In 3D Vision (3DV), 2015 3rd International Conference on, pages: 10-18, Lyon, October 2015 (inproceedings)

code YouTube pdf suppmat DOI Project Page [BibTex]

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Perception of Strength and Power of Realistic Male Characters

Wellerdiek, A. C., Breidt, M., Geuss, M. N., Streuber, S., Kloos, U., Black, M. J., Mohler, B. J.

In Proc. ACM SIGGRAPH Symposium on Applied Perception, SAP’15, pages: 7-14, ACM, New York, NY, September 2015 (inproceedings)

Abstract
We investigated the influence of body shape and pose on the perception of physical strength and social power for male virtual characters. In the first experiment, participants judged the physical strength of varying body shapes, derived from a statistical 3D body model. Based on these ratings, we determined three body shapes (weak, average, and strong) and animated them with a set of power poses for the second experiment. Participants rated how strong or powerful they perceived virtual characters of varying body shapes that were displayed in different poses. Our results show that perception of physical strength was mainly driven by the shape of the body. However, the social attribute of power was influenced by an interaction between pose and shape. Specifically, the effect of pose on power ratings was greater for weak body shapes. These results demonstrate that a character with a weak shape can be perceived as more powerful when in a high-power pose.

PDF DOI Project Page [BibTex]

Wellerdiek, A. C., Breidt, M., Geuss, M. N., Streuber, S., Kloos, U., Black, M. J., Mohler, B. J. Perception of Strength and Power of Realistic Male Characters In Proc. ACM SIGGRAPH Symposium on Applied Perception, SAP’15, pages: 7-14, ACM, New York, NY, September 2015 (inproceedings)

PDF DOI Project Page [BibTex]

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The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

Jampani, V., Nowozin, S., Loper, M., Gehler, P. V.

In Computer Vision and Image Understanding, Special Issue on Generative Models in Computer Vision and Medical Imaging, 136, pages: 32-44, Elsevier, July 2015 (inproceedings)

Abstract
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Bayesian posterior inference could then, in principle, explain the observation. While intuitively appealing, generative models for computer vision have largely failed to deliver on that promise due to the difficulty of posterior inference. As a result the community has favored efficient discriminative approaches. We still believe in the usefulness of generative models in computer vision, but argue that we need to leverage existing discriminative or even heuristic computer vision methods. We implement this idea in a principled way in our informed sampler and in careful experiments demonstrate it on challenging models which contain renderer programs as their components. The informed sampler, using simple discriminative proposals based on existing computer vision technology achieves dramatic improvements in inference. Our approach enables a new richness in generative models that was out of reach with existing inference technology.

arXiv-preprint pdf DOI Project Page [BibTex]

Jampani, V., Nowozin, S., Loper, M., Gehler, P. V. The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models In Computer Vision and Image Understanding, Special Issue on Generative Models in Computer Vision and Medical Imaging, 136, pages: 32-44, Elsevier, July 2015 (inproceedings)

arXiv-preprint pdf DOI Project Page [BibTex]

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The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose

Zuffi, S., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 3537-3546, June 2015 (inproceedings)

Abstract
We propose a new 3D model of the human body that is both realistic and part-based. The body is represented by a graphical model in which nodes of the graph correspond to body parts that can independently translate and rotate in 3D as well as deform to capture pose-dependent shape variations. Pairwise potentials define a “stitching cost” for pulling the limbs apart, giving rise to the stitched puppet model (SPM). Unlike existing realistic 3D body models, the distributed representation facilitates inference by allowing the model to more effectively explore the space of poses, much like existing 2D pictorial structures models. We infer pose and body shape using a form of particle-based max-product belief propagation. This gives the SPM the realism of recent 3D body models with the computational advantages of part-based models. We apply the SPM to two challenging problems involving estimating human shape and pose from 3D data. The first is the FAUST mesh alignment challenge (http://faust.is.tue.mpg.de/), where ours is the first method to successfully align all 3D meshes. The second involves estimating pose and shape from crude visual hull representations of complex body movements.

pdf Extended Abstract poster code/project video DOI Project Page [BibTex]

Zuffi, S., Black, M. J. The Stitched Puppet: A Graphical Model of 3D Human Shape and Pose In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 3537-3546, June 2015 (inproceedings)

pdf Extended Abstract poster code/project video DOI Project Page [BibTex]

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Displets: Resolving Stereo Ambiguities using Object Knowledge

Güney, F., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 4165-4175, June 2015 (inproceedings)

Abstract
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 paper, we therefore propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The proposed displets encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class 'car' into a superpixel based CRF framework and demonstrate its benefits on the KITTI stereo evaluation.

pdf abstract suppmat [BibTex]

Güney, F., Geiger, A. Displets: Resolving Stereo Ambiguities using Object Knowledge In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 4165-4175, June 2015 (inproceedings)

pdf abstract suppmat [BibTex]

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Object Scene Flow for Autonomous Vehicles

Menze, M., Geiger, A.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 3061-3070, IEEE, June 2015 (inproceedings)

Abstract
This paper proposes 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 each superpixel by a 3D plane as well as an index to the corresponding object. This minimal representation increases robustness and leads to a discrete-continuous CRF where the data term decomposes into pairwise potentials between superpixels and objects. Moreover, our model intrinsically segments the scene into its constituting dynamic components. We demonstrate the performance of our model on existing benchmarks as well as a novel realistic dataset with scene flow ground truth. We obtain this dataset by annotating 400 dynamic scenes from the KITTI raw data collection using detailed 3D CAD models for all vehicles in motion. Our experiments also reveal novel challenges which can't be handled by existing methods.

pdf abstract suppmat DOI [BibTex]

Menze, M., Geiger, A. Object Scene Flow for Autonomous Vehicles In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2015, pages: 3061-3070, IEEE, June 2015 (inproceedings)

pdf abstract suppmat DOI [BibTex]

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Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction

Akhter, I., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 1446-1455, June 2015 (inproceedings)

Abstract
The estimation of 3D human pose from 2D joint locations is central to many vision problems involving the analysis of people in images and video. To address the fact that the problem is inherently ill posed, many methods impose a prior over human poses. Unfortunately these priors admit invalid poses because they do not model how joint-limits vary with pose. Here we make two key contributions. First, we collected a motion capture dataset that explores a wide range of human poses. From this we learn a pose-dependent model of joint limits that forms our prior. The dataset and the prior will be made publicly available. Second, we define a general parameterization of body pose and a new, multistage, method to estimate 3D pose from 2D joint locations that uses an over-complete dictionary of human poses. Our method shows good generalization while avoiding impossible poses. We quantitatively compare our method with recent work and show state-of-the-art results on 2D to 3D pose estimation using the CMU mocap dataset. We also show superior results on manual annotations on real images and automatic part-based detections on the Leeds sports pose dataset.

pdf Extended Abstract video project/data/code poster DOI Project Page Project Page [BibTex]

Akhter, I., Black, M. J. Pose-Conditioned Joint Angle Limits for 3D Human Pose Reconstruction In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 1446-1455, June 2015 (inproceedings)

pdf Extended Abstract video project/data/code poster DOI Project Page Project Page [BibTex]

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Efficient Sparse-to-Dense Optical Flow Estimation using a Learned Basis and Layers

Wulff, J., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2015), pages: 120-130, June 2015 (inproceedings)

Abstract
We address the elusive goal of estimating optical flow both accurately and efficiently by adopting a sparse-to-dense approach. Given a set of sparse matches, we regress to dense optical flow using a learned set of full-frame basis flow fields. We learn the principal components of natural flow fields using flow computed from four Hollywood movies. Optical flow fields are then compactly approximated as a weighted sum of the basis flow fields. Our new PCA-Flow algorithm robustly estimates these weights from sparse feature matches. The method runs in under 300ms/frame on the MPI-Sintel dataset using a single CPU and is more accurate and significantly faster than popular methods such as LDOF and Classic+NL. The results, however, are too smooth for some applications. Consequently, we develop a novel sparse layered flow method in which each layer is represented by PCA-flow. Unlike existing layered methods, estimation is fast because it uses only sparse matches. We combine information from different layers into a dense flow field using an image-aware MRF. The resulting PCA-Layers method runs in 3.6s/frame, is significantly more accurate than PCA-flow and achieves state-of-the-art performance in occluded regions on MPI-Sintel.

pdf Extended Abstract Supplemental Material Poster Code Project Page Project Page [BibTex]

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Permutohedral Lattice CNNs

Kiefel, M., Jampani, V., Gehler, P. V.

In ICLR Workshop Track, May 2015 (inproceedings)

Abstract
This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.

pdf link (url) [BibTex]

Kiefel, M., Jampani, V., Gehler, P. V. Permutohedral Lattice CNNs In ICLR Workshop Track, May 2015 (inproceedings)

pdf link (url) [BibTex]

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Consensus Message Passing for Layered Graphical Models

Jampani, V., Eslami, S. M. A., Tarlow, D., Kohli, P., Winn, J.

In Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 38, pages: 425-433, JMLR Workshop and Conference Proceedings, May 2015 (inproceedings)

Abstract
Generative models provide a powerful framework for probabilistic reasoning. However, in many domains their use has been hampered by the practical difficulties of inference. This is particularly the case in computer vision, where models of the imaging process tend to be large, loopy and layered. For this reason bottom-up conditional models have traditionally dominated in such domains. We find that widely-used, general-purpose message passing inference algorithms such as Expectation Propagation (EP) and Variational Message Passing (VMP) fail on the simplest of vision models. With these models in mind, we introduce a modification to message passing that learns to exploit their layered structure by passing 'consensus' messages that guide inference towards good solutions. Experiments on a variety of problems show that the proposed technique leads to significantly more accurate inference results, not only when compared to standard EP and VMP, but also when compared to competitive bottom-up conditional models.

online pdf supplementary link (url) [BibTex]

Jampani, V., Eslami, S. M. A., Tarlow, D., Kohli, P., Winn, J. Consensus Message Passing for Layered Graphical Models In Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 38, pages: 425-433, JMLR Workshop and Conference Proceedings, May 2015 (inproceedings)

online pdf supplementary link (url) [BibTex]

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Efficient Facade Segmentation using Auto-Context

Jampani, V., Gadde, R., Gehler, P. V.

In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pages: 1038-1045, IEEE, January 2015 (inproceedings)

Abstract
In this paper we propose a system for the problem of facade segmentation. Building facades are highly structured images and consequently most methods that have been proposed for this problem, aim to make use of this strong prior information. We are describing a system that is almost domain independent and consists of standard segmentation methods. A sequence of boosted decision trees is stacked using auto-context features and learned using the stacked generalization technique. We find that this, albeit standard, technique performs better, or equals, all previous published empirical results on all available facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test time inference.

website pdf supplementary IEEE page link (url) DOI Project Page [BibTex]

Jampani, V., Gadde, R., Gehler, P. V. Efficient Facade Segmentation using Auto-Context In Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pages: 1038-1045, IEEE, January 2015 (inproceedings)

website pdf supplementary IEEE page link (url) DOI Project Page [BibTex]

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FlowCap: 2D Human Pose from Optical Flow

Romero, J., Loper, M., Black, M. J.

In Pattern Recognition, Proc. 37th German Conference on Pattern Recognition (GCPR), LNCS 9358, pages: 412-423, Springer, 2015 (inproceedings)

Abstract
We estimate 2D human pose from video using only optical flow. The key insight is that dense optical flow can provide information about 2D body pose. Like range data, flow is largely invariant to appearance but unlike depth it can be directly computed from monocular video. We demonstrate that body parts can be detected from dense flow using the same random forest approach used by the Microsoft Kinect. Unlike range data, however, when people stop moving, there is no optical flow and they effectively disappear. To address this, our FlowCap method uses a Kalman filter to propagate body part positions and ve- locities over time and a regression method to predict 2D body pose from part centers. No range sensor is required and FlowCap estimates 2D human pose from monocular video sources containing human motion. Such sources include hand-held phone cameras and archival television video. We demonstrate 2D body pose estimation in a range of scenarios and show that the method works with real-time optical flow. The results suggest that optical flow shares invariances with range data that, when complemented with tracking, make it valuable for pose estimation.

video pdf preprint Project Page Project Page [BibTex]

Romero, J., Loper, M., Black, M. J. FlowCap: 2D Human Pose from Optical Flow In Pattern Recognition, Proc. 37th German Conference on Pattern Recognition (GCPR), LNCS 9358, pages: 412-423, Springer, 2015 (inproceedings)

video pdf preprint Project Page Project Page [BibTex]

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Joint 3D Object and Layout Inference from a single RGB-D Image

(Best Paper Award)

Geiger, A., Wang, C.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 183-195, Lecture Notes in Computer Science, Springer International Publishing, 2015 (inproceedings)

Abstract
Inferring 3D objects and the layout of indoor scenes from a single RGB-D image captured with a Kinect camera is a challenging task. Towards this goal, we propose a high-order graphical model and jointly reason about the layout, objects and superpixels in the image. In contrast to existing holistic approaches, our model leverages detailed 3D geometry using inverse graphics and explicitly enforces occlusion and visibility constraints for respecting scene properties and projective geometry. We cast the task as MAP inference in a factor graph and solve it efficiently using message passing. We evaluate our method with respect to several baselines on the challenging NYUv2 indoor dataset using 21 object categories. Our experiments demonstrate that the proposed method is able to infer scenes with a large degree of clutter and occlusions.

pdf suppmat video project DOI [BibTex]

Geiger, A., Wang, C. Joint 3D Object and Layout Inference from a single RGB-D Image In German Conference on Pattern Recognition (GCPR), 9358, pages: 183-195, Lecture Notes in Computer Science, Springer International Publishing, 2015 (inproceedings)

pdf suppmat video project DOI [BibTex]

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3D Object Class Detection in the Wild

Pepik, B., Stark, M., Gehler, P., Ritschel, T., Schiele, B.

In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2015 (inproceedings)

Project Page [BibTex]

Pepik, B., Stark, M., Gehler, P., Ritschel, T., Schiele, B. 3D Object Class Detection in the Wild In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, 2015 (inproceedings)

Project Page [BibTex]

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Discrete Optimization for Optical Flow

Menze, M., Heipke, C., Geiger, A.

In German Conference on Pattern Recognition (GCPR), 9358, pages: 16-28, Springer International Publishing, 2015 (inproceedings)

Abstract
We propose to look at large-displacement optical flow from a discrete point of view. Motivated by the observation that sub-pixel accuracy is easily obtained given pixel-accurate optical flow, we conjecture that computing the integral part is the hardest piece of the problem. Consequently, we formulate optical flow estimation as a discrete inference problem in a conditional random field, followed by sub-pixel refinement. Naive discretization of the 2D flow space, however, is intractable due to the resulting size of the label set. In this paper, we therefore investigate three different strategies, each able to reduce computation and memory demands by several orders of magnitude. Their combination allows us to estimate large-displacement optical flow both accurately and efficiently and demonstrates the potential of discrete optimization for optical flow. We obtain state-of-the-art performance on MPI Sintel and KITTI.

pdf suppmat project DOI [BibTex]

Menze, M., Heipke, C., Geiger, A. Discrete Optimization for Optical Flow In German Conference on Pattern Recognition (GCPR), 9358, pages: 16-28, Springer International Publishing, 2015 (inproceedings)

pdf suppmat project DOI [BibTex]

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Joint 3D Estimation of Vehicles and Scene Flow

Menze, M., Heipke, C., Geiger, A.

In Proc. of the ISPRS Workshop on Image Sequence Analysis (ISA), 2015 (inproceedings)

Abstract
Three-dimensional reconstruction of dynamic scenes is an important prerequisite for applications like mobile robotics or autonomous driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.

PDF [BibTex]

Menze, M., Heipke, C., Geiger, A. Joint 3D Estimation of Vehicles and Scene Flow In Proc. of the ISPRS Workshop on Image Sequence Analysis (ISA), 2015 (inproceedings)

PDF [BibTex]

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Smooth Loops from Unconstrained Video

Sevilla-Lara, L., Wulff, J., Sunkavalli, K., Shechtman, E.

In Computer Graphics Forum (Proceedings of EGSR), 34(4):99-107, 2015 (inproceedings)

Abstract
Converting unconstrained video sequences into videos that loop seamlessly is an extremely challenging problem. In this work, we take the first steps towards automating this process by focusing on an important subclass of videos containing a single dominant foreground object. Our technique makes two novel contributions over previous work: first, we propose a correspondence-based similarity metric to automatically identify a good transition point in the video where the appearance and dynamics of the foreground are most consistent. Second, we develop a technique that aligns both the foreground and background about this transition point using a combination of global camera path planning and patch-based video morphing. We demonstrate that this allows us to create natural, compelling, loopy videos from a wide range of videos collected from the internet.

pdf link (url) DOI Project Page [BibTex]

Sevilla-Lara, L., Wulff, J., Sunkavalli, K., Shechtman, E. Smooth Loops from Unconstrained Video In Computer Graphics Forum (Proceedings of EGSR), 34(4):99-107, 2015 (inproceedings)

pdf link (url) DOI Project Page [BibTex]