3,712 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
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Predicting the Next Best View for 3D Mesh Refinement
3D reconstruction is a core task in many applications such as robot
navigation or sites inspections. Finding the best poses to capture part of the
scene is one of the most challenging topic that goes under the name of Next
Best View. Recently, many volumetric methods have been proposed; they choose
the Next Best View by reasoning over a 3D voxelized space and by finding which
pose minimizes the uncertainty decoded into the voxels. Such methods are
effective, but they do not scale well since the underlaying representation
requires a huge amount of memory. In this paper we propose a novel mesh-based
approach which focuses on the worst reconstructed region of the environment
mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and
an energy function over the worst regions of the mesh which takes into account
the mutual parallax with respect to the previous cameras, the angle of
incidence of the viewing ray to the surface and the visibility of the region.
We test our approach over a well known dataset and achieve state-of-the-art
results.Comment: 13 pages, 5 figures, to be published in IAS-1
3D Dynamic Scene Reconstruction from Multi-View Image Sequences
A confirmation report outlining my PhD research plan is presented. The PhD research topic is 3D dynamic scene reconstruction from multiple view image sequences. Chapter 1 describes the motivation and research aims. An overview of the progress in the past year is included. Chapter 2 is a review of volumetric scene reconstruction techniques and Chapter 3 is an in-depth description of my proposed reconstruction method. The theory behind the proposed volumetric scene reconstruction method is also presented, including topics in projective geometry, camera calibration and energy minimization. Chapter 4 presents the research plan and outlines the future work planned for the next two years
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