Contribution Addresses the light-field sampling and reconstruction problem with deep learning techniques and offline datasets. Proposes a novel R-network that explores the image and direction spaces of the radiance field to effectively filter and reconstruct the incident radiance field. Presents a novel RL-based Q-network to guide the adaptive rendering process. Related Work Image-Space Methods Light field Reconstruction Methods Light field Adaptive Sampling Methods Filtering using DNN DRL R ...

Contribution An efficient temporal coherence-based scheduling algorithm A domain assignment algorithm Assign domains to all nodes according to the ray transmission information among domains in the previous frame A runtime scheduling algorithm Estimate each domain’s pre-loading prior based on the node’s current situation and information of the previous frame A new virtual portal structure to record the radiance of rays passing through domains in the previous frame In the current frame, pred ...

Author: Yang Zhou, Lifan Wu, Ravi Ramamoorthi, Ling-Qi Yan Paper: Link Video: Presentation: IntroductionThere are generally two approaches to render images: Rasterization: Project scene geometry onto the screen and breaks the geometry into pixels Fast but prone to aliasing Ray Tracing: Cast rays into the scene and bounces them stochastically to find paths connecting the light and the camera High-quality but is slow and noisy Main ProblemMain problem for both approaches: point sampling ...

IntroductionEdge sampling is a method to calculate the derivative of the ray tracing result w.r.t. some scene parameters (including camera pose, scene geometry, material and light parameters). The key idea of edge sampling is dividing the gradient integral into smooth (interior) and discontinuous (boundary) regions. For the smooth part, we use automatic differentiation. For the discontinuous, we use edge sampling to capture the changes at boundaries. Condition: Focus on triangle meshes Assume ...