提出更真实且高效的动态视觉传感器像素模型，支持高动态范围场景模拟。
Towards a physically realistic computationally efficient DVS pixel model
- 基于电路分析推导大信号微分方程，参数由实测与仿真拟合。
- 采用首达时理论实现高效随机事件生成，时间步长提升1000倍以上。
- 兼顾物理真实性与计算效率，适合大规模场景仿真与参数优化。
动态视觉传感器（DVS）事件相机模型是预测相机响应、优化偏置参数和生成真实感模拟数据集的重要工具。现有模型虽有应用价值，但在高动态范围（HDR）复杂场景下缺乏足够真实性，同时在阵列级场景仿真中难以兼顾计算效率。本文提出一种基于电路分析推导的大信号微分方程构建的物理可解释像素模型，其参数通过像素实测数据与电路仿真进行拟合。结合基于首达时理论的高效随机事件生成机制，可在时间步长超过此前方法1000倍的情况下仍保持精确噪声建模能力，显著提升模拟效率而不牺牲物理真实性，为复杂场景下的实时仿真与系统优化提供新方案。
原文摘要 · Abstract (English)
Dynamic Vision Sensor (DVS) event camera models are important tools for predicting camera response, optimizing biases, and generating realistic simulated datasets. Existing DVS models have been useful, but have not demonstrated high realism for challenging HDR scenes combined with adequate computational efficiency for array-level scene simulation. This paper reports progress towards a physically realistic and computationally efficient DVS model based on large-signal differential equations derived from circuit analysis, with parameters fitted from pixel measurements and circuit simulation. These are combined with an efficient stochastic event generation mechanism based on first-passage-time theory, allowing accurate noise generation with timesteps greater than 1000x longer than previous methods
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