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Rendering Point Clouds with Compute Shaders
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Two page abstract at arXiv

This repository contains two variations on how to render point clouds with compute shaders that are up to ten times faster than gl.drawArrays(GL_POINT, ...). In the folders compute and compute_hqs you will find a regular non-anti-aliased version and a high-quality splatting version.

Please note that benchmarking results were obtained with the original order of points in our test files and for pixel sizes of 1 pixel. The order of points has a significant impact on performance and shuffling points severely reduces the performance gains of our compute shader approach. It can even make the high-quality compute shader version slower than the regular gl.drawArrays(GL_POINT, ...). In-depth benchmarking is subject to future work.

This repository is an excerpt of the compute shader rasterization part of

Video (YouTube):


Up to 2-10 times faster than GL_POINT.

  • render.cs: Encodes depth and colors into a 64 bit integer, and stores the closest fragment into an SSBO with atomicMin.
  • resolve.cs: Transfers color values from the SSBO to an actual OpenGL texture.


Up to 2-3 times faster than GL_POINT.

A compute shader implementation of High-Quality Surface Splatting on Today’s GPUs[2]. Instead of rendering only the closest point, this approach computes the average of all points within a certain depth-range, which leads to pretty good anti-aliasing within a pixel. Currently doesn't do anti-aliasing between pixels, though.

  • render_depth.cs: Creates a depth-buffer using the basic compute approach.
  • render_attribute.cs: Computes the sum of colors of all points that are at most 1% behind the closest point in a pixel. Also counts how many points contribute to the sum.
  • resolve.cs: Computes the average color of contributing points via: sum(colors) / length(colors). Writes the result into an OpenGL texture.


  • Our renderer uses OpenGL 4.5 via glew and glfw, C++, V8 Javascript Engine. See
  • Tested on an RTX 2080 TI and a GTX 1060.
  • Points in the test files we used were somewhat ordered, altough we do not know the means by which they were ordered. Shuffling drastically changes the results.
  • Evaluated for a size of 1 pixel per point. The compute shader based approach scales roughly linearly with the point size/fragment count, whereas GL_POINT scales much better. So our approach is ideal for a size of 1 pixel per point, but the usefulness diminishes for sizes larger than 2x2 pixels.
  • The Retz model was reduced to 100M points on the GTX 1060 (3GB) because of memory constraints.
Model #Points GPU AtomicMin High-Quality Splatting GL_POINT
Heidentor 26M 2080 TI 1.64 ms 3.37 ms 5.71 ms
1060 GTX 4.88 ms 11.78 ms 13.60 ms
Retz 145M 2080 TI 6.41 ms 12.95 ms 34.04 ms
100M 1060 GTX 14.32 ms 31.76 ms 58.82 ms
Morro Bay 117M 2080 TI 5.87 ms 15.48 ms 60.26 ms


  • [1] Markus Schütz, Michael Wimmer, "Rendering Point Clouds with Compute Shaders", arXiv
  • [2] Mario Botsch, Alexander Hornung, Matthias Zwicker, Leif Kobbelt, "High-Quality Surface Splatting on Today’s GPUs", Eurographics Symposium on Point-Based Graphics (2005)
  • [3] Christian M Günther, Thomas Kanzok, Lars Linsen, and Paul Rosenthal. 2013. "A GPGPU-based Pipeline for Accelerated Rendering of Point Clouds." Journal of WSCG 21 (2013), 153–161.
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