# High-Performance and Tunable Stereo Reconstruction

arXiv: https://arxiv.org/abs/1511.00758 · HTML: https://mlsanity.com/paper/1511.00758/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2015.

## Built on by (most cited first, 3 of 3)

- [These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion](https://mlsanity.com/paper/2411.03717/) (2024)
- [Smooth Mesh Estimation from Depth Data using Non-Smooth Convex Optimization](https://mlsanity.com/paper/2108.02957/) (2021)
- [UAV-Borne Mapping Algorithms for Low-Altitude and High-Speed Drone Applications](https://mlsanity.com/paper/2401.06407/) (2024)

## Abstract (arXiv)

Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immediate environment and perform tasks within it. In this work, we propose a high-performance and tunable stereo disparity estimation method, with a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can potentially enable robots to quickly reconstruct their immediate surroundings and maneuver at high-speeds. Our key contribution is a disparity estimation algorithm that iteratively approximates the scene depth via a piece-wise planar mesh from stereo imagery, with a fast depth validation step for semi-dense reconstruction. The mesh is initially seeded with sparsely matched keypoints, and is recursively tessellated and refined as needed (via a resampling stage), to provide the desired stereo disparity accuracy. The inherent simplicity and speed of our approach, with the ability to tune it to a desired reconstruction quality and runtime performance makes it a compelling solution for applications in high-speed vehicles.