A Confidence Weighted Real-Time Depth Filter for 3D Reconstruction

Abstract : 3D reconstruction is an important technique in the environmental perception and rehabilitation process. With the help of active depth-aware sensors, such as Kinect from Microsoft and SwissRanger, the depth map can be captured at the video frame rate together with color information to enable the real-time reconstruction. Particularly, it features prominently in the activity recognition and remote rehabilitation. Unfortunately, the coarseness of the depth map make it difficult to extract the detailed information in 3D reconstruction of the scene and tracking of thin objects. Especially, geometric distortions occur around the edge of an object. Therefore, this paper presents a confidence weighted real-time depth filter for the edge recovery to reduce the extra artifacts due to the uncertainty of each depth measurement. Also the intensity of depth map is taken into account to optimize the weighting term in the algorithm. Moreover, the GPU implementation guarantees the high computational efficiency for the real-time applications. Experimental results are shown to illustrate the performance of the proposed method by the comparisons with the traditional methods.
Document type :
Conference papers
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-01614990
Contributor : Hal Ifip <>
Submitted on : Wednesday, October 11, 2017 - 4:57:48 PM
Last modification on : Wednesday, October 11, 2017 - 5:00:32 PM
Long-term archiving on : Friday, January 12, 2018 - 3:23:52 PM

File

433802_1_En_23_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Zhenzhou Shao, Zhiping Shi, Ying Qu, Yong Guan, Hongxing Wei, et al.. A Confidence Weighted Real-Time Depth Filter for 3D Reconstruction. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.222-231, ⟨10.1007/978-3-319-48390-0_23⟩. ⟨hal-01614990⟩

Share

Metrics

Record views

101

Files downloads

92