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Since single image SVBRDF estimation is a highly ill-posed problem, such an optimization is prone to overfitting. Our main contribution is to address this problem by introducing a training mechanism that takes the test-time optimization into account. Specifically, we train our network by minimizing the training loss after one or more gradient updates with the test loss. By training the network in this manner, we ensure that the network does not overfit to the input image during the test-time optimization process. Additionally, we propose a learned reflectance loss to augment the typically used rendering loss during the test-time optimization. We do so by using an auxiliary network that estimates pseudo ground truth reflectance parameters and train it in combination with the main network. Our approach is able to converge with a small number of iterations of the test-time optimization and produces better results compared to the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3550454.3555495","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T21:19:07Z","timestamp":1669843147000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Look-Ahead Training with Learned Reflectance Loss for Single-Image SVBRDF Estimation"],"prefix":"10.1145","volume":"41","author":[{"given":"Xilong","family":"Zhou","sequence":"first","affiliation":[{"name":"Texas A&amp;M University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nima Khademi","family":"Kalantari","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925917"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766967"},{"key":"e_1_2_2_3_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJGven05Y7","author":"Antoniou Antreas","year":"2019","unstructured":"Antreas Antoniou, Harrison Edwards, and Amos Storkey. 2019. 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