Synthetic SPAD Depth Sensing Dataset

The synthetic SPAD depth sensing dataset is specifically designed to train and evaluate neural network architectures for histogram-less single-photon depth estimation tasks. It simulates diverse real-world conditions with comprehensive parameter combinations, including target distances, background illumination, target reflectivity, and atmospheric conditions. The dataset consists of two parts: a test set with fixed parameter combinations for consistent evaluation, and a training/validation set with randomly generated parameters to enhance model generalization. The dataset provides raw timestamps rather than processed point clouds. Each parameter combination contains 2000 independent timestamp sequences that have undergone basic coincidence detection. For detailed specifications, please refer to the paper and the supplementary material.

本合成式单光子雪崩二极管（Single-Photon Avalanche Diode, SPAD）深度传感数据集，专为训练与评估面向无直方图单光子深度估计任务的神经网络架构而设计。该数据集通过涵盖目标距离、背景光照、目标反射率与大气条件在内的全维度参数组合，模拟多样化的真实场景环境。本数据集包含两个部分：一是采用固定参数组合的测试集，用于开展标准化一致性评估；二是采用随机生成参数的训练/验证集，以提升模型的泛化能力。数据集提供原始时间戳数据，而非已预处理的点云数据。每组参数组合均包含2000条经过基础符合探测（coincidence detection）处理的独立时间戳序列。若需了解详细参数规格，请参阅相关论文与补充材料。