The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Modeling Kinect Sensor Noise for Improved 3D Reconstruction and Tracking
Abstract
We contribute an empirically derived noise model for the Kinect sensor. We systematically measure both lateral and axial noise distributions, as a function of both distance and angle of the Kinect to an observed surface. The derived noise model can be used to filter Kinect depth maps for a variety of applications. Our second contribution applies our derived noise model to the KinectFusion system to extend filtering, volumetric fusion, and pose estimation within the pipeline. Qualitative results show our method allows reconstruction of finer details and the ability to reconstruct smaller objects and thinner surfaces. Quantitative results also show our method improves pose estimation accuracy.
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Cites
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“This allows us to apply more efficient and simpler imagebased filtering methods, such as [10] to achieve similar results to full 3D filtering.”
MethodObject modeling by registration of multiple range images
Chen, Y., & Medioni, G. (2002) · Proceedings. 1991 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.1991.132043 · Cited by 1,409
Cited by
Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning
Oleynikova, H., Taylor, Z., Fehr, M., Siegwart, R., & Nieto, J. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202315 · Cited by 658
Kinect range sensing: Structured-light versus Time-of-Flight Kinect
Sarbolandi, H., Lefloch, D., & Kolb, A. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.05.006 · Cited by 389
Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion
Keller, M., Lefloch, D., Lambers, M., Izadi, S., Weyrich, T., & Kolb, A. (2013) · 2013 International Conference on 3D Vision · doi:10.1109/3dv.2013.9 · Cited by 384
Self-citationKinect v2 for mobile robot navigation: Evaluation and modeling
Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R., Hutter, M., & Siegwart, R. (2015) · 2015 International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2015.7251485 · Cited by 314
An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments
Schmid, L., Pantic, M., Khanna, R., Ott, L., Siegwart, R., & Nieto, J. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2969191 · Cited by 308
Metadata
- Authors
- Nguyen, C. V., Izadi, S., & Lovell, D.
- Venue
- 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission
- Publisher
- IEEE
- Published
- 2012-10
- Pages
- 524-530
- OpenAlex
- W2016285927
- Cited by
- 334
- References
- 2
- FWCI
- 43.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Nguyen, C. V., Izadi, S., & Lovell, D. (2012). Modeling Kinect Sensor Noise for Improved 3D Reconstruction and Tracking. 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission. https://doi.org/10.1109/3dimpvt.2012.84
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