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   Meng, Xiaoxu; Du, Ruofei; Zwicker, Matthias; Varshney, Amitabh (July 2018, Proceedings of the ACM on Computer Graphics and Interactive Techniques)
2. Path-Space Differentiable Rendering

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   Zhang, Cheng; Miller, Bailey; Yan, Kan; Gkioulekas, Ioannis; Zhao, Shuang (July 2020, ACM transactions on graphics)

   Physics-based differentiable rendering, the estimation of derivatives of ra- diometric measures with respect to arbitrary scene parameters, has a diverse array of applications from solving analysis-by-synthesis problems to train- ing machine learning pipelines incorporating forward rendering processes. Unfortunately, general-purpose differentiable rendering remains challenging due to the lack of efficient estimators as well as the need to identify and handle complex discontinuities such as visibility boundaries. In this paper, we show how path integrals can be differentiated with respect to arbitrary differentiable changes of a scene. We provide a detailed theoretical analysis of this process and establish new differentiable rendering formulations based on the resulting differential path integrals. Our path- space differentiable rendering formulation allows the design of new Monte Carlo estimators that offer significantly better efficiency than state-of-the-art methods in handling complex geometric discontinuities and light transport phenomena such as caustics. 

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3. Differentiable time-gated rendering

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   Wu, Lifan; Cai, Guangyan; Ramamoorthi, Ravi; Zhao, Shuang (December 2021, ACM Transactions on Graphics)

   The continued advancements of time-of-flight imaging devices have enabled new imaging pipelines with numerous applications. Consequently, several forward rendering techniques capable of accurately and efficiently simulating these devices have been introduced. However, general-purpose differentiable rendering techniques that estimate derivatives of time-of-flight images are still lacking. In this paper, we introduce a new theory of differentiable time-gated rendering that enjoys the generality of differentiating with respect to arbitrary scene parameters. Our theory also allows the design of advanced Monte Carlo estimators capable of handling cameras with near-delta or discontinuous time gates. We validate our theory by comparing derivatives generated with our technique and finite differences. Further, we demonstrate the usefulness of our technique using a few proof-of-concept inverse-rendering examples that simulate several time-of-flight imaging scenarios. 

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4. Characterizing the Effects of Haptic Rendering Parameter Variations on Perceived Kinesthetic Rendering Accuracy

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   To understand how the realism of a kinesthetic haptic rendering is affected by the accurate selection of the rendering model parameters, we conducted a preliminary user study where subjects compared three real-world objects to their equivalent haptic rendering. The subjects rated the rendering realism as the model parameters were varied about their nominal values. The results suggest that the required accuracy of various haptic rendering parameters is not equally important when considering the perceived realism. 

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5. Doppler Time-of-Flight Rendering

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   Kim, Juhyeon; Jarosz, Wojciech; Gkioulekas, Ioannis; Pediredla, Adithya (December 2023, ACM Transactions on Graphics)

   We introduce Doppler time-of-flight (D-ToF) rendering, an extension of ToF rendering for dynamic scenes, with applications in simulating D-ToF cameras. D-ToF cameras use high-frequency modulation of illumination and exposure, and measure the Doppler frequency shift to compute the radial velocity of dynamic objects. The time-varying scene geometry and high-frequency modulation functions used in such cameras make it challenging to accurately and efficiently simulate their measurements with existing ToF rendering algorithms. We overcome these challenges in a twofold manner: To achieve accuracy, we derive path integral expressions for D-ToF measurements under global illumination and form unbiased Monte Carlo estimates of these integrals. To achieve efficiency, we develop a tailored time-path sampling technique that combines antithetic time sampling with correlated path sampling. We show experimentally that our sampling technique achieves up to two orders of magnitude lower variance compared to naive time-path sampling. We provide an open-source simulator that serves as a digital twin for D-ToF imaging systems, allowing imaging researchers, for the first time, to investigate the impact of modulation functions, material properties, and global illumination on D-ToF imaging performance. 

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