# Rendering Point Clouds with Compute Shaders and Vertex Order Optimization

arXiv: https://arxiv.org/abs/2104.07526 · HTML: https://mlsanity.com/paper/2104.07526/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2021.

## Built on by (most cited first, 14 of 14)

- [A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets](https://mlsanity.com/paper/2406.12080/) (2024)
- [RVT-2: Learning Precise Manipulation from Few Demonstrations](https://mlsanity.com/paper/2406.08545/) (2024)
- [StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering](https://mlsanity.com/paper/2402.00525/) (2024)
- [TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2401.06003/) (2024)
- [VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Points](https://mlsanity.com/paper/2410.17932/) (2024)
- [Faster-GS: Analyzing and Improving Gaussian Splatting Optimization](https://mlsanity.com/paper/2602.09999/) (2026)
- [Frequency-Modulated Point Cloud Rendering with Easy Editing](https://mlsanity.com/paper/2303.07596/) (2023)
- [INPC: Implicit Neural Point Clouds for Radiance Field Rendering](https://mlsanity.com/paper/2403.16862/) (2024)
- [ADOP: Approximate Differentiable One-Pixel Point Rendering](https://mlsanity.com/paper/2110.06635/) (2021)
- [SpatialVAM:Spatial-Aware Multi-View Video Diffusion as a Data-Efficient Robot Policy](https://mlsanity.com/paper/2604.03181/) (2026)
- [UV-free Texture Generation with Denoising and Geodesic Heat Diffusions](https://mlsanity.com/paper/2408.16762/) (2024)
- [NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization](https://mlsanity.com/paper/2407.19097/) (2024)
- [Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation](https://mlsanity.com/paper/2409.09921/) (2024)
- [CRISTAL: Real-time Camera Registration in Static LiDAR Scans using Neural Rendering](https://mlsanity.com/paper/2511.16349/) (2025)

## Abstract (arXiv)

While commodity GPUs provide a continuously growing range of features and sophisticated methods for accelerating compute jobs, many state-of-the-art solutions for point cloud rendering still rely on the provided point primitives (GL_POINTS, POINTLIST, ...) of graphics APIs for image synthesis. In this paper, we present several compute-based point cloud rendering approaches that outperform the hardware pipeline by up to an order of magnitude and achieve significantly better frame times than previous compute-based methods. Beyond basic closest-point rendering, we also introduce a fast, high-quality variant to reduce aliasing. We present and evaluate several variants of our proposed methods with different flavors of optimization, in order to ensure their applicability and achieve optimal performance on a range of platforms and architectures with varying support for novel GPU hardware features. During our experiments, the observed peak performance was reached rendering 796 million points (12.7GB) at rates of 62 to 64 frames per second (50 billion points per second, 802GB/s) on an RTX 3090 without the use of level-of-detail structures. We further introduce an optimized vertex order for point clouds to boost the efficiency of GL_POINTS by a factor of 5x in cases where hardware rendering is compulsory. We compare different orderings and show that Morton sorted buffers are faster for some viewpoints, while shuffled vertex buffers are faster in others. In contrast, combining both approaches by first sorting according to Morton-code and shuffling the resulting sequence in batches of 128 points leads to a vertex buffer layout with high rendering performance and low sensitivity to viewpoint changes.