QRF Dataset

The QRF dataset is designed to support research in quantum-native photorealistic scene rendering. It consists of high-fidelity 3D indoor and outdoor environments captured from multiple calibrated viewpoints, with detailed annotations of geometry, material properties, and lighting conditions. Each scene is processed into quantum-compatible representations for training and evaluating Quantum Radiance Fields (QRF), which leverage quantum circuits, activation functions, and quantum volume rendering.This dataset serves as a benchmark for evaluating the efficiency and realism of quantum implicit representations. It includes both low-frequency and high-frequency features to challenge classical rendering models and demonstrate the expressive power of quantum processing. The dataset facilitates experimentation in tasks such as view synthesis, radiance prediction, and ray-based integration under a quantum computing paradigm. By providing carefully curated and diverse scene data, the QRF dataset enables rigorous testing of quantum advantages in 3D vision and computational graphics.

QRF数据集旨在支撑量子原生（quantum-native）真实感场景渲染领域的研究工作。该数据集包含从多个经过校准的视点采集的高保真三维室内外场景，附带几何结构、材质属性与光照条件的详细标注。所有场景均被处理为量子兼容表征，用于训练与评估量子辐射场（Quantum Radiance Fields，QRF）——此类模型依托量子电路、激活函数与量子体绘制技术实现功能。本数据集可作为基准测试集，用于评估量子隐式表征的运行效率与渲染真实感。数据集同时涵盖低频与高频特征，既能够对经典渲染模型发起性能挑战，也可用于展现量子处理的表达能力。该数据集支持在量子计算范式下开展视图合成、辐射预测与基于光线的积分等多项任务的实验研究。通过提供经过精心筛选且场景类型多样的数据集，QRF数据集能够在三维视觉与计算图形学领域，对量子优势展开严谨的测试验证。