Graphics Programming weekly - Issue 294 - July 2nd 2023

Debugging CUDA More Efficiently with NVIDIA Compute Sanitizer

  • This article discusses a code sanitizer in CUDA that is similar to one used in C++
  • The tool can detect race conditions, memory violations, uninitialized memory access, and thread synchronization issues.

Behind the Pretty Frames: Diablo IV

  • the article provides an in-depth study of a Diablo IV frame rendering on PC
  • presents the different stages and passes of the frame
  • additionally contains a section of some technical ticks & tricks that were employed from art and programming

An Approximate Mie Scattering Function for Fog and Cloud Rendering

  • The paper introduces a new method for representing scattering with varying particle sizes using uniform parameters
  • The method combines Henyey-Greenstein and Draine’s phase derivation
  • The paper provides an analytic evaluation of the method
  • The quality of the method is compared to other methods

[video] Make Your Renders Unnecessarily Complicated

  • the video shows an humerous overview of what is required to model a camera, with functioning lenes, filters etc
  • shows the difficulties and complexities that camera’s following a real-world model face
  • finally shows a number of images that got generated using this method

Signed Distance Function (Field)

  • the article dicusses how to use signed distance functions to render a circle
  • show render a circle with antialiasing, drop-shadows, and edge borders.

Neural Intersection Function

  • the paper introduces a replacement for bottom-level BVH traversal based on neural network techniques
  • presents performance and quality comparison
  • additionally also shows how the presented technique is view and scene dependent

[pdf] Path tracing in Production - The Path of Water

  • the Siggraph 2023 course notes discuss the challenges faced related to water rendering on Avatar: The Path of water
  • gives an overview of the challenges faced, a solid foundational background for the underlying models
  • presents practical implementation strategies used
  • additionally presents several unsolved problems

[video] Convolutions and adding random variables, visually explained

  • the video explains the concept of convolution functions/filters
  • starts with distinct distributions and generalizes from there into continuous functions
  • develops two separate ways to visualize the computations and show how they allow different questions to be answered more neatly

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