Learning Sample-Based Monte Carlo Denoising from Noisy Training Data

Monte Carlo rendering allows for the production of high-quality photorealistic images of 3D scenes. However, producing noise-free images can take a considerable amount of compute resources. To lessen this burden and speed up the rendering process while maintaining similar quality, a lower-sample count image can be rendered and then denoised after rendering with image-space denoising methods. These methods are widely used in industry, and have recently enabled advancements in areas such as real-time ray tracing. While hand-tuned denoisers are available, the most successful denoising methods are based on machine learning with deep convolutional neural networks (CNNs). These denoisers are trained on large datasets of rendered images, consisting of pairs of low-sample count noisy images and the corresponding high-sample count reference images. Unfortunately, generating these datasets can be prohibitively expensive because of the cost of rendering thousands of high-sample count reference images. A potential solution to this problem comes from the Noise2Noise method, where denoisers can be learned solely from noisy training data. Lehtinen et al. applied their technique to Monte Carlo denoising, and were able to achieve similar performance to using clean reference images. However, their model was a proof of concept, and it is unclear whether the technique would work equally well with state-of-the-art Monte Carlo denoising methods. The authors also do not test their hypothesis that better results could be achieved by training on the additional noisy training data that could be generated with the same compute budget that was previously allocated to generating clean training data. Finally, it remains to be seen whether the authors' suggested parameters are equally effective when Noise2Noise is used with different denoising methods. In this thesis, I answer the above questions by applying Noise2Noise to a state-of-the-art Monte Carlo denoising algorithm called Sample-Based Monte-Carlo Denoising (SBMC). I adapt the SBMC scene generator to produce a dataset of noisy image pairs, use this dataset to train an SBMC-like CNN, and conduct experiments to determine the impact of various parameters on the performance of the denoiser. My results show that the Noise2Noise technique can be effectively applied to a state-of-the-art Monte Carlo denoising algorithm. I achieved comparable results to the original implementation at a significantly lower cost. I find that using additional training data can further improve these results, although more investigation is needed in this area. Finally, I detail the parameters that were necessary to achieve these results

The insights from the crowd: drawing inferences from many approaches to key empirical questions in international business

In this crowdsourced initiative, 57 independent analysts used the same longitudinal dataset to address four major empirical questions in international business. For all four research questions, different analysts obtained substantial estimates in opposite directions, meaning that they could have drawn any conclusion at all had they conducted the project alone. Aggregating across the results obtained by different analysts pointed to an overall answer for 2 of the 4 research questions, although for one of the two questions the evidence was more suggestive than conclusive. That said, the variability in results was not simply random, and could in some cases be meaningfully explained. Choices regarding how to operationalize variables played an important role in determining the empirical results, and expert analysts were more likely to report large positive effects. Rather than exhibiting a bias to confirm their pre-existing beliefs, analysts appeared to rationally update their beliefs considering the evidence. Overall, these findings empirically demonstrate the role of subjective researcher choices in shaping results in international business research yet also show that it is still possible to draw meaningful conclusions in science. We advocate for an open science of international business in which the consequences of subjective analytic choices are rendered as transparent as possible.</p