Measurement-Adaptive Light-Cone Transform for Robust Non-Line-of-Sight Imaging
Practical non-line-of-sight (NLOS) measurements are affected by noise and deviations from idealized light transport, which can degrade reconstruction with conventional light-cone transform (LCT) methods.
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Key points
- Experiments on synthetic data and real measurements from different NLOS systems demonstrate improved reconstruction quality.
- Controlled evaluations further assess robustness to temporal-response broadening and reduced acquisition time.
- MALCT also preserves scene structures with spatial sampling grids down to $32\times32$, supporting its potential for NLOS imaging under limited acquisition budgets.
Sources (1)
- [1]Measurement-Adaptive Light-Cone Transform for Robust Non-Line-of-Sight ImagingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 02:47 AM
Practical non-line-of-sight (NLOS) measurements are affected by noise and deviations from idealized light transport, which can degrade reconstruction with conventional light-cone transform (LCT) methods.
Experiments on synthetic data and real measurements from different NLOS systems demonstrate improved reconstruction quality.
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