59,603 research outputs found
Temporally Coherent General Dynamic Scene Reconstruction
Existing techniques for dynamic scene reconstruction from multiple
wide-baseline cameras primarily focus on reconstruction in controlled
environments, with fixed calibrated cameras and strong prior constraints. This
paper introduces a general approach to obtain a 4D representation of complex
dynamic scenes from multi-view wide-baseline static or moving cameras without
prior knowledge of the scene structure, appearance, or illumination.
Contributions of the work are: An automatic method for initial coarse
reconstruction to initialize joint estimation; Sparse-to-dense temporal
correspondence integrated with joint multi-view segmentation and reconstruction
to introduce temporal coherence; and a general robust approach for joint
segmentation refinement and dense reconstruction of dynamic scenes by
introducing shape constraint. Comparison with state-of-the-art approaches on a
variety of complex indoor and outdoor scenes, demonstrates improved accuracy in
both multi-view segmentation and dense reconstruction. This paper demonstrates
unsupervised reconstruction of complete temporally coherent 4D scene models
with improved non-rigid object segmentation and shape reconstruction and its
application to free-viewpoint rendering and virtual reality.Comment: Submitted to IJCV 2019. arXiv admin note: substantial text overlap
with arXiv:1603.0338
Reversible Jump Metropolis Light Transport using Inverse Mappings
We study Markov Chain Monte Carlo (MCMC) methods operating in primary sample
space and their interactions with multiple sampling techniques. We observe that
incorporating the sampling technique into the state of the Markov Chain, as
done in Multiplexed Metropolis Light Transport (MMLT), impedes the ability of
the chain to properly explore the path space, as transitions between sampling
techniques lead to disruptive alterations of path samples. To address this
issue, we reformulate Multiplexed MLT in the Reversible Jump MCMC framework
(RJMCMC) and introduce inverse sampling techniques that turn light paths into
the random numbers that would produce them. This allows us to formulate a novel
perturbation that can locally transition between sampling techniques without
changing the geometry of the path, and we derive the correct acceptance
probability using RJMCMC. We investigate how to generalize this concept to
non-invertible sampling techniques commonly found in practice, and introduce
probabilistic inverses that extend our perturbation to cover most sampling
methods found in light transport simulations. Our theory reconciles the
inverses with RJMCMC yielding an unbiased algorithm, which we call Reversible
Jump MLT (RJMLT). We verify the correctness of our implementation in canonical
and practical scenarios and demonstrate improved temporal coherence, decrease
in structured artifacts, and faster convergence on a wide variety of scenes
3D Scene Annotation for Efficient Rendering on Mobile Devices
This paper presents a new approach for efficient 3D rendering on mobile devices, where selective rendering can be achieved with the help of 3D scene annotation. By taking advantage of first person environments in most 3D applications, we are able to annotate the flooring details of the 3D space. This allows 3D environments to be interfaced using a higher level view of objects. With the higher level of scene understanding, it is possible to determine which 3D objects are not required for loading or rendering based on the viewer’s location and its surrounding constraints
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