Monocular, Real-Time Surface Reconstruction using Dynamic Level of Detail
File(s)zienkiewicz_etal_3dv2016.pdf (9.09 MB)
Accepted version
Author(s)
Zienkiewicz, J
Tsiotsios, C
Davison, AJ
Leutenegger, S
Type
Conference Paper
Abstract
We present a scalable, real-time capable method for robust
surface reconstruction that explicitly handles multiple
scales. As a monocular camera browses a scene, our
algorithm processes images as they arrive and incrementally
builds a detailed surface model. While most of the
existing reconstruction approaches rely on volumetric or
point-cloud representations of the environment, we perform
depth-map and colour fusion directly into a multi-resolution
triangular mesh that can be adaptively tessellated using
the concept of Dynamic Level of Detail. Our method relies
on least-squares optimisation, which enables a probabilistically
sound and principled formulation of the fusion
algorithm. We demonstrate that our method is capable of
obtaining high quality, close-up reconstruction, as well as
capturing overall scene geometry, while being memory and
computationally efficient.
surface reconstruction that explicitly handles multiple
scales. As a monocular camera browses a scene, our
algorithm processes images as they arrive and incrementally
builds a detailed surface model. While most of the
existing reconstruction approaches rely on volumetric or
point-cloud representations of the environment, we perform
depth-map and colour fusion directly into a multi-resolution
triangular mesh that can be adaptively tessellated using
the concept of Dynamic Level of Detail. Our method relies
on least-squares optimisation, which enables a probabilistically
sound and principled formulation of the fusion
algorithm. We demonstrate that our method is capable of
obtaining high quality, close-up reconstruction, as well as
capturing overall scene geometry, while being memory and
computationally efficient.
Date Issued
2016-12-19
Date Acceptance
2016-09-02
Citation
2016 Fourth International Conference on 3D Vision (3DV), 2016
Publisher
IEEE
Journal / Book Title
2016 Fourth International Conference on 3D Vision (3DV)
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Dyson Technology Limited
Dyson Technology Limited
Grant Number
PO 4500098215
PO 4500378543
Source
International Conference on 3DVision
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Engineering
Publication Status
Published
Start Date
2016-10-25
Finish Date
2016-10-28
Coverage Spatial
Stanford, CA, USA
OA Location
https://www.doc.ic.ac.uk/~jz4411/data/zienkiewicz_etal_3dv2016.pdf