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Gradient Flow Evolution for 3D Fusion From a Single Depth Sensor

Authors
Kang, JiwooLee, SeongminJang, MingyuLee, Sanghoon
Issue Date
Apr-2022
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Keywords
Three-dimensional displays; Surface reconstruction; Pipelines; Strain; Cameras; Reliability; Real-time systems; 3D reconstruction; signed distance field; implicit representation; non-rigid deformation; incremental reconstruction
Citation
IEEE Transactions on Circuits and Systems for Video Technology, v.32, no.4, pp.2211 - 2225
Journal Title
IEEE Transactions on Circuits and Systems for Video Technology
Volume
32
Number
4
Start Page
2211
End Page
2225
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/151304
DOI
10.1109/tcsvt.2021.3089695
ISSN
1051-8215
Abstract
We present a novel real-time framework for non-rigid 3D reconstruction that is robust to noise, camera poses, and large deformation from a single depth camera. KinectFusion has achieved high-quality 3D object reconstructions in real-time by implicitly representing an object’s surface with a signed distance field (SDF) representation from a single depth camera. Many studies for incremental reconstruction have been presented since then, with the surface estimation improving over time. Previous works primarily focused on improving conventional SDF matching and deformation schemes. In contrast to these works, the proposed framework tackles the problem of temporal inconsistency caused by SDF approximation and fusion to manipulate SDFs and reconstruct a target more accurately over time. In our reconstruction pipeline, we introduce a refinement evolution method, where an erroneous SDF from a depth sensor is recovered more accurately in a few iterations by propagating erroneous SDF values from the surface. Reliable gradients of refined SDFs enable more accurate non-rigid tracking of a target object. Furthermore, we propose a level-set evolution for SDF fusion, enabling SDFs to be manipulated stably in the reconstruction pipeline over time. The proposed methods are fully parallelizable and can be executed in real-time. Q
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