12,407 research outputs found
Self-correction of 3D reconstruction from multi-view stereo images
We present a self-correction approach to improving the
3D reconstruction of a multi-view 3D photogrammetry system.
The self-correction approach has been able to repair
the reconstructed 3D surface damaged by depth discontinuities.
Due to self-occlusion, multi-view range images
have to be acquired and integrated into a watertight nonredundant
mesh model in order to cover the extended surface
of an imaged object. The integrated surface often suffers
from “dent” artifacts produced by depth discontinuities
in the multi-view range images. In this paper we propose
a novel approach to correcting the 3D integrated surface
such that the dent artifacts can be repaired automatically.
We show examples of 3D reconstruction to demonstrate the
improvement that can be achieved by the self-correction
approach. This self-correction approach can be extended
to integrate range images obtained from alternative range
capture devices
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
We present an approach that uses a multi-camera system to train fine-grained
detectors for keypoints that are prone to occlusion, such as the joints of a
hand. We call this procedure multiview bootstrapping: first, an initial
keypoint detector is used to produce noisy labels in multiple views of the
hand. The noisy detections are then triangulated in 3D using multiview geometry
or marked as outliers. Finally, the reprojected triangulations are used as new
labeled training data to improve the detector. We repeat this process,
generating more labeled data in each iteration. We derive a result analytically
relating the minimum number of views to achieve target true and false positive
rates for a given detector. The method is used to train a hand keypoint
detector for single images. The resulting keypoint detector runs in realtime on
RGB images and has accuracy comparable to methods that use depth sensors. The
single view detector, triangulated over multiple views, enables 3D markerless
hand motion capture with complex object interactions.Comment: CVPR 201
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