Kornia-rs: A Low-Level 3D Computer Vision Library In Rust
- URL: http://arxiv.org/abs/2505.12425v1
- Date: Sun, 18 May 2025 13:50:00 GMT
- Title: Kornia-rs: A Low-Level 3D Computer Vision Library In Rust
- Authors: Edgar Riba, Jian Shi, Aditya Kumar, Andrew Shen, Gary Bradski,
- Abstract summary: textitkornia-rs is a high-performance 3D computer vision library written entirely in native Rust.<n>textitkornia-rs adopts a statically-typed tensor system and a modular set of crates, providing efficient image I/O, image processing and 3D operations.
- Score: 6.567185366423734
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present \textit{kornia-rs}, a high-performance 3D computer vision library written entirely in native Rust, designed for safety-critical and real-time applications. Unlike C++-based libraries like OpenCV or wrapper-based solutions like OpenCV-Rust, \textit{kornia-rs} is built from the ground up to leverage Rust's ownership model and type system for memory and thread safety. \textit{kornia-rs} adopts a statically-typed tensor system and a modular set of crates, providing efficient image I/O, image processing and 3D operations. To aid cross-platform compatibility, \textit{kornia-rs} offers Python bindings, enabling seamless and efficient integration with Rust code. Empirical results show that \textit{kornia-rs} achieves a 3~ 5 times speedup in image transformation tasks over native Rust alternatives, while offering comparable performance to C++ wrapper-based libraries. In addition to 2D vision capabilities, \textit{kornia-rs} addresses a significant gap in the Rust ecosystem by providing a set of 3D computer vision operators. This paper presents the architecture and performance characteristics of \textit{kornia-rs}, demonstrating its effectiveness in real-world computer vision applications.
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