24,931 research outputs found
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow
methods simultaneously estimate the 3D geometry and motion of the observed
scene. Many existing approaches use superpixels for regularization, but may
predict inconsistent shapes and motions inside rigidly moving objects. We
instead assume that scenes consist of foreground objects rigidly moving in
front of a static background, and use semantic cues to produce pixel-accurate
scene flow estimates. Our cascaded classification framework accurately models
3D scenes by iteratively refining semantic segmentation masks, stereo
correspondences, 3D rigid motion estimates, and optical flow fields. We
evaluate our method on the challenging KITTI autonomous driving benchmark, and
show that accounting for the motion of segmented vehicles leads to
state-of-the-art performance.Comment: International Conference on 3D Vision (3DV), 2017 (oral presentation
General Dynamic Scene Reconstruction from Multiple View Video
This paper introduces a general approach to dynamic scene reconstruction from
multiple moving cameras without prior knowledge or limiting constraints on the
scene structure, appearance, or illumination. Existing techniques for dynamic
scene reconstruction from multiple wide-baseline camera views primarily focus
on accurate reconstruction in controlled environments, where the cameras are
fixed and calibrated and background is known. These approaches are not robust
for general dynamic scenes captured with sparse moving cameras. Previous
approaches for outdoor dynamic scene reconstruction assume prior knowledge of
the static background appearance and structure. The primary contributions of
this paper are twofold: an automatic method for initial coarse dynamic scene
segmentation and reconstruction without prior knowledge of background
appearance or structure; and a general robust approach for joint segmentation
refinement and dense reconstruction of dynamic scenes from multiple
wide-baseline static or moving cameras. Evaluation is performed on a variety of
indoor and outdoor scenes with cluttered backgrounds and multiple dynamic
non-rigid objects such as people. Comparison with state-of-the-art approaches
demonstrates improved accuracy in both multiple view segmentation and dense
reconstruction. The proposed approach also eliminates the requirement for prior
knowledge of scene structure and appearance
Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixel Measurement Models
This paper proposes a method for fusing data acquired by a ToF camera and a stereo pair based on a model for depth measurement by ToF cameras which accounts also for depth discontinuity artifacts due to the mixed pixel effect. Such model is exploited within both a ML and a MAP-MRF frameworks for ToF and stereo data fusion. The proposed MAP-MRF framework is characterized by site-dependent range values, a rather important feature since it can be used both to improve the accuracy and to decrease the computational complexity of standard MAP-MRF approaches. This paper, in order to optimize the site dependent global cost function characteristic of the proposed MAP-MRF approach, also introduces an extension to Loopy Belief Propagation which can be used in other contexts. Experimental data validate the proposed ToF measurements model and the effectiveness of the proposed fusion techniques
Alternating Back-Propagation for Generator Network
This paper proposes an alternating back-propagation algorithm for learning
the generator network model. The model is a non-linear generalization of factor
analysis. In this model, the mapping from the continuous latent factors to the
observed signal is parametrized by a convolutional neural network. The
alternating back-propagation algorithm iterates the following two steps: (1)
Inferential back-propagation, which infers the latent factors by Langevin
dynamics or gradient descent. (2) Learning back-propagation, which updates the
parameters given the inferred latent factors by gradient descent. The gradient
computations in both steps are powered by back-propagation, and they share most
of their code in common. We show that the alternating back-propagation
algorithm can learn realistic generator models of natural images, video
sequences, and sounds. Moreover, it can also be used to learn from incomplete
or indirect training data
Community detection and stochastic block models: recent developments
The stochastic block model (SBM) is a random graph model with planted
clusters. It is widely employed as a canonical model to study clustering and
community detection, and provides generally a fertile ground to study the
statistical and computational tradeoffs that arise in network and data
sciences.
This note surveys the recent developments that establish the fundamental
limits for community detection in the SBM, both with respect to
information-theoretic and computational thresholds, and for various recovery
requirements such as exact, partial and weak recovery (a.k.a., detection). The
main results discussed are the phase transitions for exact recovery at the
Chernoff-Hellinger threshold, the phase transition for weak recovery at the
Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial
recovery, the learning of the SBM parameters and the gap between
information-theoretic and computational thresholds.
The note also covers some of the algorithms developed in the quest of
achieving the limits, in particular two-round algorithms via graph-splitting,
semi-definite programming, linearized belief propagation, classical and
nonbacktracking spectral methods. A few open problems are also discussed
Temporally coherent 4D reconstruction of complex dynamic scenes
This paper presents an approach for reconstruction of 4D temporally coherent
models of complex dynamic scenes. No prior knowledge is required of scene
structure or camera calibration allowing reconstruction from multiple moving
cameras. Sparse-to-dense temporal correspondence is integrated with joint
multi-view segmentation and reconstruction to obtain a complete 4D
representation of static and dynamic objects. Temporal coherence is exploited
to overcome visual ambiguities resulting in improved reconstruction of complex
scenes. Robust joint segmentation and reconstruction of dynamic objects is
achieved by introducing a geodesic star convexity constraint. Comparative
evaluation is performed on a variety of unstructured indoor and outdoor dynamic
scenes with hand-held cameras and multiple people. This demonstrates
reconstruction of complete temporally coherent 4D scene models with improved
nonrigid object segmentation and shape reconstruction.Comment: To appear in The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 2016 . Video available at:
https://www.youtube.com/watch?v=bm_P13_-Ds
Automatic Objects Removal for Scene Completion
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as 3D scene reconstruction and
other big data applications. However, this is not an easy task due to the fact
the retrieved photos are neither aligned nor calibrated. Furthermore, with the
occlusion of unexpected foreground objects like people, vehicles, it is even
more challenging to find feature correspondences and reconstruct realistic
scenes. In this paper, we propose a structure based image completion algorithm
for object removal that produces visually plausible content with consistent
structure and scene texture. We use an edge matching technique to infer the
potential structure of the unknown region. Driven by the estimated structure,
texture synthesis is performed automatically along the estimated curves. We
evaluate the proposed method on different types of images: from highly
structured indoor environment to the natural scenes. Our experimental results
demonstrate satisfactory performance that can be potentially used for
subsequent big data processing: 3D scene reconstruction and location
recognition.Comment: 6 pages, IEEE International Conference on Computer Communications
(INFOCOM 14), Workshop on Security and Privacy in Big Data, Toronto, Canada,
201
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