# stdgpu: Efficient STL-like Data Structures on the GPU

arXiv: https://arxiv.org/abs/1908.05936 · HTML: https://mlsanity.com/paper/1908.05936/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [ASH: A Modern Framework for Parallel Spatial Hashing in 3D Perception](https://mlsanity.com/paper/2110.00511/) (2021)
- [Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments](https://mlsanity.com/paper/2412.00291/) (2024)
- [Rendering and Tracking the Directional TSDF: Modeling Surface Orientation for Coherent Maps](https://mlsanity.com/paper/2108.08115/) (2021)
- [Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP](https://mlsanity.com/paper/2301.12796/) (2023)

## Abstract (arXiv)

Tremendous advances in parallel computing and graphics hardware opened up several novel real-time GPU applications in the fields of computer vision, computer graphics as well as augmented reality (AR) and virtual reality (VR). Although these applications built upon established open-source frameworks that provide highly optimized algorithms, they often come with custom self-written data structures to manage the underlying data. In this work, we present stdgpu, an open-source library which defines several generic GPU data structures for fast and reliable data management. Rather than abandoning previous established frameworks, our library aims to extend them, therefore bridging the gap between CPU and GPU computing. This way, it provides clean and familiar interfaces and integrates seamlessly into new as well as existing projects. We hope to foster further developments towards unified CPU and GPU computing and welcome contributions from the community.