5,037 research outputs found
Scalable Dense Monocular Surface Reconstruction
This paper reports on a novel template-free monocular non-rigid surface
reconstruction approach. Existing techniques using motion and deformation cues
rely on multiple prior assumptions, are often computationally expensive and do
not perform equally well across the variety of data sets. In contrast, the
proposed Scalable Monocular Surface Reconstruction (SMSR) combines strengths of
several algorithms, i.e., it is scalable with the number of points, can handle
sparse and dense settings as well as different types of motions and
deformations. We estimate camera pose by singular value thresholding and
proximal gradient. Our formulation adopts alternating direction method of
multipliers which converges in linear time for large point track matrices. In
the proposed SMSR, trajectory space constraints are integrated by smoothing of
the measurement matrix. In the extensive experiments, SMSR is demonstrated to
consistently achieve state-of-the-art accuracy on a wide variety of data sets.Comment: International Conference on 3D Vision (3DV), Qingdao, China, October
201
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
We introduce a multi-scale framework for low-level vision, where the goal is
estimating physical scene values from image data---such as depth from stereo
image pairs. The framework uses a dense, overlapping set of image regions at
multiple scales and a "local model," such as a slanted-plane model for stereo
disparity, that is expected to be valid piecewise across the visual field.
Estimation is cast as optimization over a dichotomous mixture of variables,
simultaneously determining which regions are inliers with respect to the local
model (binary variables) and the correct co-ordinates in the local model space
for each inlying region (continuous variables). When the regions are organized
into a multi-scale hierarchy, optimization can occur in an efficient and
parallel architecture, where distributed computational units iteratively
perform calculations and share information through sparse connections between
parents and children. The framework performs well on a standard benchmark for
binocular stereo, and it produces a distributional scene representation that is
appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page:
http://www.ttic.edu/chakrabarti/consensus
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Recent work has shown that optical flow estimation can be formulated as a
supervised learning task and can be successfully solved with convolutional
networks. Training of the so-called FlowNet was enabled by a large
synthetically generated dataset. The present paper extends the concept of
optical flow estimation via convolutional networks to disparity and scene flow
estimation. To this end, we propose three synthetic stereo video datasets with
sufficient realism, variation, and size to successfully train large networks.
Our datasets are the first large-scale datasets to enable training and
evaluating scene flow methods. Besides the datasets, we present a convolutional
network for real-time disparity estimation that provides state-of-the-art
results. By combining a flow and disparity estimation network and training it
jointly, we demonstrate the first scene flow estimation with a convolutional
network.Comment: Includes supplementary materia
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
We propose a novel approach for optical flow estimation , targeted at large
displacements with significant oc-clusions. It consists of two steps: i) dense
matching by edge-preserving interpolation from a sparse set of matches; ii)
variational energy minimization initialized with the dense matches. The
sparse-to-dense interpolation relies on an appropriate choice of the distance,
namely an edge-aware geodesic distance. This distance is tailored to handle
occlusions and motion boundaries -- two common and difficult issues for optical
flow computation. We also propose an approximation scheme for the geodesic
distance to allow fast computation without loss of performance. Subsequent to
the dense interpolation step, standard one-level variational energy
minimization is carried out on the dense matches to obtain the final flow
estimation. The proposed approach, called Edge-Preserving Interpolation of
Correspondences (EpicFlow) is fast and robust to large displacements. It
significantly outperforms the state of the art on MPI-Sintel and performs on
par on Kitti and Middlebury
Multi-Scale 3D Scene Flow from Binocular Stereo Sequences
Scene flow methods estimate the three-dimensional motion field for points in the world, using multi-camera video data. Such methods combine multi-view reconstruction with motion estimation. This paper describes an alternative formulation for dense scene flow estimation that provides reliable results using only two cameras by fusing stereo and optical flow estimation into a single coherent framework. Internally, the proposed algorithm generates probability distributions for optical flow and disparity. Taking into account the uncertainty in the intermediate stages allows for more reliable estimation of the 3D scene flow than previous methods allow. To handle the aperture problems inherent in the estimation of optical flow and disparity, a multi-scale method along with a novel region-based technique is used within a regularized solution. This combined approach both preserves discontinuities and prevents over-regularization – two problems commonly associated with the basic multi-scale approaches. Experiments with synthetic and real test data demonstrate the strength of the proposed approach.National Science Foundation (CNS-0202067, IIS-0208876); Office of Naval Research (N00014-03-1-0108
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