We previously investigated color constancy in photorealistic virtual reality (VR) and developed a Deep Neural Network (DNN) that predicts reflectance from rendered images. Here, we combine both approaches to compare and study a model and human performance with respect to established color constancy mechanisms: local surround, maximum flux and spatial mean. Rather than evaluating the model against physical ground truth, model performance was assessed using the same achromatic object selection task employed in the human experiments. The model, a ResNet based U-Net from our previous work, was pre-trained on rendered images to predict surface reflectance. We then applied transfer learning, fine-tuning only the network's decoder on images from the baseline VR condition. To parallel the human experiment, the model's output was used to perform the same achromatic object selection task across all conditions. Results show a strong correspondence between the model and human behavior. Both achieved high constancy under baseline conditions and showed similar, condition-dependent performance declines when the local surround or spatial mean color cues were removed.

翻译：我们先前在照片级真实感的虚拟现实（VR）中研究了颜色恒常性，并开发了一种能够从渲染图像预测反射率的深度神经网络（DNN）。在此，我们结合这两种方法，针对已确立的颜色恒常性机制——局部环境、最大通量和空间均值——来比较和研究模型与人类的性能表现。我们并非根据物理真实值来评估模型，而是采用与人类实验相同的消色差物体选择任务来评估模型性能。该模型是我们先前工作中基于ResNet的U-Net，已在渲染图像上进行预训练以预测表面反射率。随后，我们应用迁移学习，仅针对基线VR条件下的图像对网络的解码器进行微调。为了与人类实验保持一致，我们利用模型的输出来执行所有条件下的相同消色差物体选择任务。结果显示，模型与人类行为之间存在高度一致性。两者在基线条件下均实现了较高的恒常性，并且在移除局部环境或空间均值颜色线索时，都表现出相似且依赖于条件的性能下降。