Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Matrix computations (3rd ed.)
An Introduction to Variational Methods for Graphical Models
Machine Learning
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Direct Recovery of Planar-Parallax from Multiple Frames
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements
Journal of Mathematical Imaging and Vision
Essential Matrix Estimation Using Gauss-Newton Iterations on a Manifold
International Journal of Computer Vision
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning long-range vision for autonomous off-road driving
Journal of Field Robotics - Special Issue on LAGR Program, Part II
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Generic and real-time structure from motion using local bundle adjustment
Image and Vision Computing
Optimization Algorithms on Matrix Manifolds
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Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular 3D scene modeling and inference: understanding multi-object traffic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Joint estimation of motion, structure and geometry from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Dense, robust, and accurate motion field estimation from stereo image sequences in real-time
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
SIAM Journal on Imaging Sciences
Efficient large-scale stereo matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
A Variational Framework for Structure from Motion in Omnidirectional Image Sequences
Journal of Mathematical Imaging and Vision
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
Dense versus Sparse Approaches for Estimating the Fundamental Matrix
International Journal of Computer Vision
Fast cost-volume filtering for visual correspondence and beyond
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
IEEE Transactions on Robotics
Are we ready for autonomous driving? The KITTI vision benchmark suite
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Dense reconstruction on-the-fly
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
DTAM: Dense tracking and mapping in real-time
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Continuous markov random fields for robust stereo estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Parallel generalized thresholding scheme for live dense geometry from a handheld camera
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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We present an approach to jointly estimating camera motion and dense structure of a static scene in terms of depth maps from monocular image sequences in driver-assistance scenarios. At each instant of time, only two consecutive frames are processed as input data of a joint estimator that fully exploits second-order information of the corresponding optimization problem and effectively copes with the non-convexity due to both the imaging geometry and the manifold of motion parameters. Additionally, carefully designed Gaussian approximations enable probabilistic inference based on locally varying confidence and globally varying sensitivity due to the epipolar geometry, with respect to the high-dimensional depth map estimation. Embedding the resulting joint estimator in an online recursive framework achieves a pronounced spatio-temporal filtering effect and robustness. We evaluate hundreds of images taken from a car moving at speed up to 100 km/h and being part of a publicly available benchmark data set. The results compare favorably with two alternative settings: stereo based scene reconstruction and camera motion estimation in batch mode using multiple frames. They, however, require a calibrated camera pair or storage for more than two frames, which is less attractive from a technical viewpoint than the proposed monocular and recursive approach. In addition to real data, a synthetic sequence is considered which provides reliable ground truth.