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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2303.02608 |
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| _version_ | 1866910411423481856 |
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| author | Tsuji, Yuta Yatagawa, Tatsuya Kubo, Hiroyuki Morishima, Shigeo |
| author_facet | Tsuji, Yuta Yatagawa, Tatsuya Kubo, Hiroyuki Morishima, Shigeo |
| contents | This paper presents an algorithm to obtain an event-based video from noisy frames given by physics-based Monte Carlo path tracing over a synthetic 3D scene. Given the nature of dynamic vision sensor (DVS), rendering event-based video can be viewed as a process of detecting the changes from noisy brightness values. We extend a denoising method based on a weighted local regression (WLR) to detect the brightness changes rather than applying denoising to every pixel. Specifically, we derive a threshold to determine the likelihood of event occurrence and reduce the number of times to perform the regression. Our method is robust to noisy video frames obtained from a few path-traced samples. Despite its efficiency, our method performs comparably to or even better than an approach that exhaustively denoises every frame. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_02608 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising Tsuji, Yuta Yatagawa, Tatsuya Kubo, Hiroyuki Morishima, Shigeo Computer Vision and Pattern Recognition Graphics This paper presents an algorithm to obtain an event-based video from noisy frames given by physics-based Monte Carlo path tracing over a synthetic 3D scene. Given the nature of dynamic vision sensor (DVS), rendering event-based video can be viewed as a process of detecting the changes from noisy brightness values. We extend a denoising method based on a weighted local regression (WLR) to detect the brightness changes rather than applying denoising to every pixel. Specifically, we derive a threshold to determine the likelihood of event occurrence and reduce the number of times to perform the regression. Our method is robust to noisy video frames obtained from a few path-traced samples. Despite its efficiency, our method performs comparably to or even better than an approach that exhaustively denoises every frame. |
| title | Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2303.02608 |