Kornia-rs: A Low-Level 3D Computer Vision Library In Rust
Abstract
We present 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, kornia-rs is built from the ground up to leverage Rust's ownership model and type system for memory and thread safety. 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, kornia-rs offers Python bindings, enabling seamless and efficient integration with Rust code. Empirical results show that 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, 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 kornia-rs, demonstrating its effectiveness in real-world computer vision applications.
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.