8,786 research outputs found
Scalable 3D video of dynamic scenes
In this paper we present a scalable 3D video framework for capturing and rendering dynamic scenes. The acquisition system is based on multiple sparsely placed 3D video bricks, each comprising a projector, two grayscale cameras, and a color camera. Relying on structured light with complementary patterns, texture images and pattern-augmented views of the scene are acquired simultaneously by time-multiplexed projections and synchronized camera exposures. Using space-time stereo on the acquired pattern images, high-quality depth maps are extracted, whose corresponding surface samples are merged into a view-independent, point-based 3D data structure. This representation allows for effective photo-consistency enforcement and outlier removal, leading to a significant decrease of visual artifacts and a high resulting rendering quality using EWA volume splatting. Our framework and its view-independent representation allow for simple and straightforward editing of 3D video. In order to demonstrate its flexibility, we show compositing techniques and spatiotemporal effect
Multiple image view synthesis for free viewpoint video applications
Interactive audio-visual (AV) applications such as free viewpoint video (FVV) aim to enable unrestricted spatio-temporal navigation within multiple camera environments. Current virtual viewpoint view synthesis solutions for FVV are either purely image-based implying large information redundancy; or involve reconstructing complex 3D models of the scene. In this paper we present a new multiple image view synthesis algorithm that only requires camera parameters and disparity maps. The multi-view synthesis (MVS) approach can be used in any multi-camera environment and is scalable as virtual views can be created given 1 to N of the available video inputs, providing a means to gracefully handle scenarios where camera inputs decrease or increase over time. The algorithm identifies and selects only the best quality surface areas from available reference images, thereby reducing perceptual errors in virtual view reconstruction. Experimental results are presented and verified using both objective (PSNR) and subjective comparisons
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
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
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