216,755 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
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
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Given the recent advances in depth prediction from Convolutional Neural
Networks (CNNs), this paper investigates how predicted depth maps from a deep
neural network can be deployed for accurate and dense monocular reconstruction.
We propose a method where CNN-predicted dense depth maps are naturally fused
together with depth measurements obtained from direct monocular SLAM. Our
fusion scheme privileges depth prediction in image locations where monocular
SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.
We demonstrate the use of depth prediction for estimating the absolute scale of
the reconstruction, hence overcoming one of the major limitations of monocular
SLAM. Finally, we propose a framework to efficiently fuse semantic labels,
obtained from a single frame, with dense SLAM, yielding semantically coherent
scene reconstruction from a single view. Evaluation results on two benchmark
datasets show the robustness and accuracy of our approach.Comment: 10 pages, 6 figures, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR), Hawaii, USA, June, 2017. The first two
authors contribute equally to this pape
Dense 3D Object Reconstruction from a Single Depth View
In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs
the complete 3D structure of a given object from a single arbitrary depth view
using generative adversarial networks. Unlike existing work which typically
requires multiple views of the same object or class labels to recover the full
3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation
of a depth view of the object as input, and is able to generate the complete 3D
occupancy grid with a high resolution of 256^3 by recovering the
occluded/missing regions. The key idea is to combine the generative
capabilities of autoencoders and the conditional Generative Adversarial
Networks (GAN) framework, to infer accurate and fine-grained 3D structures of
objects in high-dimensional voxel space. Extensive experiments on large
synthetic datasets and real-world Kinect datasets show that the proposed
3D-RecGAN++ significantly outperforms the state of the art in single view 3D
object reconstruction, and is able to reconstruct unseen types of objects.Comment: TPAMI 2018. Code and data are available at:
https://github.com/Yang7879/3D-RecGAN-extended. This article extends from
arXiv:1708.0796
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