Web: http://arxiv.org/abs/2201.10910

Jan. 27, 2022, 2:10 a.m. | Jakeoung Koo, Abderrahim Halimi, Stephen McLaughlin

cs.CV updates on arXiv.org arxiv.org

Deploying 3D single-photon Lidar imaging in real world applications faces
multiple challenges including imaging in high noise environments. Several
algorithms have been proposed to address these issues based on statistical or
learning-based frameworks. Statistical methods provide rich information about
the inferred parameters but are limited by the assumed model correlation
structures, while deep learning methods show state-of-the-art performance but
limited inference guarantees, preventing their extended use in critical
applications. This paper unrolls a statistical Bayesian algorithm into a new
deep …

algorithm arxiv bayesian deep lidar systems

More from arxiv.org / cs.CV updates on arXiv.org

Senior Data Analyst

@ Fanatics Inc | Remote - New York

Data Engineer - Search

@ Cytora | United Kingdom - Remote

Product Manager, Technical - Data Infrastructure and Streaming

@ Nubank | Berlin

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Principal Data Scientist

@ Zuora | Remote

Data Engineer

@ Veeva Systems | Pennsylvania - Fort Washington