Rethinking SSIM-Based Optimization in Neural Field Training

The Structural Similarity (SSIM) index is a widely used metric for evaluating image quality, with broad applications in areas such as image restoration, 3D reconstruction, and novel view synthesis. A number of previous works have introduced SSIM-based optimization into neural field training to enhance the model's performance. Despite its widespread use, there has been limited research on how to effectively incorporate SSIM loss into the training process. In this work, we explore this gap and provide insights into the role of SSIM loss in neural field training. Our key finding is that SSIM loss is particularly beneficial during the early phase of training, before the model fully learns the luminance information. We show that SSIM loss acts as an effective “guidance” mechanism in the initial training phase, and removing it after the model has learned the luminance does not harm the final performance-in fact, it may improve it. Our experiments demonstrate the effectiveness of our strategy, offering new insights into how SSIM loss can be more efficiently used in neural field training. We believe these findings will not only enhance SSIM's application in neural field training but also inspire further research into more adaptive loss functions for deep learning models.