“Adaptive Sampling for Monte-Carlo Event Imagery Rendering” by Manabe, Yatagawa, Morishima and Kubo
Conference:
Type(s):
Title:
- Adaptive Sampling for Monte-Carlo Event Imagery Rendering
Session/Category Title: Images, Video & Computer Vision
Presenter(s)/Author(s):
Abstract:
The novel event-based camera simulation system based on physically accurate Monte Carlo path tracing with adaptive path sampling based on the probability of event occurrence.
References:
[1]
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[2]
Yuhuang Hu, Shih-Chii Liu, and Tobi Delbruck. 2021. v2e: From Video Frames to Realistic DVS Events. 1312?1321. https://doi.org/10.1109/cvprw53098.2021.00144
[3]
Patrick Lichtsteiner, Christoph Posch, and Tobi Delbruck. 2008. A 128 ? 128 120 dB 15 ? s Latency Asynchronous Temporal Contrast Vision Sensor. IEEE Journal of Solid-State Circuits 43, 2 (2008), 566?576. https://doi.org/10.1109/jssc.2007.914337
[4]
Henri Rebecq, Daniel Gehrig, and Davide Scaramuzza. 2018. ESIM: an open event camera simulator. In Conference on Robot Learning. 969?982.
[5]
Fabrice Rousselle, Claude Knaus, and Matthias Zwicker. 2012. Adaptive Rendering with Non-Local Means Filtering. ACM Transactions on Graphics 31, 6 (2012), 1?11. https://doi.org/10.1145/2366145.2366214
[6]
Yuta Tsuji, Tatsuya Yatagawa, Hiroyuki Kubo, and Shigeo Morishima. 2023. Event-Based Camera Simulation Using Monte Carlo Path Tracing with Adaptive Denoising. In Proceedings of the IEEE International Conference on Image Processing (ICIP). IEEE. https://doi.org/10.1109/icip49359.2023.10222771
