4,829 research outputs found
From Multiview Image Curves to 3D Drawings
Reconstructing 3D scenes from multiple views has made impressive strides in
recent years, chiefly by correlating isolated feature points, intensity
patterns, or curvilinear structures. In the general setting - without
controlled acquisition, abundant texture, curves and surfaces following
specific models or limiting scene complexity - most methods produce unorganized
point clouds, meshes, or voxel representations, with some exceptions producing
unorganized clouds of 3D curve fragments. Ideally, many applications require
structured representations of curves, surfaces and their spatial relationships.
This paper presents a step in this direction by formulating an approach that
combines 2D image curves into a collection of 3D curves, with topological
connectivity between them represented as a 3D graph. This results in a 3D
drawing, which is complementary to surface representations in the same sense as
a 3D scaffold complements a tent taut over it. We evaluate our results against
truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an
overview of the supplementary material available at
multiview-3d-drawing.sourceforge.ne
Morphing a Stereogram into Hologram
This paper develops a simple and fast method to reconstruct reality from
stereoscopic images. We bring together ideas from robust optical flow
techniques, morphing deformations and lightfield 3D rendering in order to
create unsupervised multiview images of a scene. The reconstruction algorithm
provides a good visualization of the virtual 3D imagery behind stereograms upon
display on a headset-free Looking Glass 3D monitor. We discuss the possibility
of applying the method for live 3D streaming optimized via an associated lookup
table.Comment: PDF, 8 pages, 4 Fig
Depth map compression via 3D region-based representation
In 3D video, view synthesis is used to create new virtual views between
encoded camera views. Errors in the coding of the depth maps introduce
geometry inconsistencies in synthesized views. In this paper, a new 3D plane
representation of the scene is presented which improves the performance of
current standard video codecs in the view synthesis domain. Two image segmentation
algorithms are proposed for generating a color and depth segmentation.
Using both partitions, depth maps are segmented into regions without
sharp discontinuities without having to explicitly signal all depth edges. The
resulting regions are represented using a planar model in the 3D world scene.
This 3D representation allows an efficient encoding while preserving the 3D
characteristics of the scene. The 3D planes open up the possibility to code
multiview images with a unique representation.Postprint (author's final draft
Multiview photometric stereo
This paper addresses the problem of obtaining complete, detailed reconstructions of textureless shiny objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with previous photometric stereo techniques, ours is not limited to a single viewpoint but produces accurate reconstructions in full 3D. A number of images of the object are obtained from multiple viewpoints, under varying lighting conditions. Starting from the silhouettes, the algorithm recovers camera motion and constructs the object's visual hull. This is then used to recover the illumination and initialize a multiview photometric stereo scheme to obtain a closed surface reconstruction. There are two main contributions in this paper: First, we describe a robust technique to estimate light directions and intensities and, second, we introduce a novel formulation of photometric stereo which combines multiple viewpoints and, hence, allows closed surface reconstructions. The algorithm has been implemented as a practical model acquisition system. Here, a quantitative evaluation of the algorithm on synthetic data is presented together with complete reconstructions of challenging real objects. Finally, we show experimentally how, even in the case of highly textured objects, this technique can greatly improve on correspondence-based multiview stereo results
Joint Reconstruction of Multi-view Compressed Images
The distributed representation of correlated multi-view images is an
important problem that arise in vision sensor networks. This paper concentrates
on the joint reconstruction problem where the distributively compressed
correlated images are jointly decoded in order to improve the reconstruction
quality of all the compressed images. We consider a scenario where the images
captured at different viewpoints are encoded independently using common coding
solutions (e.g., JPEG, H.264 intra) with a balanced rate distribution among
different cameras. A central decoder first estimates the underlying correlation
model from the independently compressed images which will be used for the joint
signal recovery. The joint reconstruction is then cast as a constrained convex
optimization problem that reconstructs total-variation (TV) smooth images that
comply with the estimated correlation model. At the same time, we add
constraints that force the reconstructed images to be consistent with their
compressed versions. We show by experiments that the proposed joint
reconstruction scheme outperforms independent reconstruction in terms of image
quality, for a given target bit rate. In addition, the decoding performance of
our proposed algorithm compares advantageously to state-of-the-art distributed
coding schemes based on disparity learning and on the DISCOVER
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