Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms first identify common features across images and then minimize reconstruction errors after estimating the shape of the terrain. This bundle adjustment (BA) step optimizes around a single, simplifying scalar value that obfuscates many possible causes of reconstruction errors (e.g., initial estimate of the position and orientation of the camera, lighting conditions, ease of feature detection in the terrain). Reconstruction errors can lead to inaccurate scientific inferences or endanger a spacecraft exploring a remote environment. To address this challenge, we present VECTOR, a visual analysis tool that improves error inspection for stereo reconstruction BA. VECTOR provides analysts with previously unavailable visibility into feature locations, camera pose, and computed 3D points. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission.

A comprehensive uncertainty, baseline, and noise analysis in computing 3D points using a recent L1-based triangulation algorithm is presented. This method is shown to be not only faster and more accurate than its main competitor, linear triangulation, but also more stable under noise and baseline changes. A Monte Carlo analysis of covariance and a confidence ellipsoid analysis were performed over a large range of baselines and noise levels for different camera configurations, to compare performance between angular error-based and linear triangulation. Furthermore, the effect of baseline and noise was analyzed for true multi-view triangulation versus pair wise stereo fusion. Results on real and synthetic data show that L1 angular error-based triangulation has a positive effect on confidence ellipsoids, lowers covariance values and results in more-accurate pair wise and multi-view triangulation, for varying numbers of cameras and configurations.

This paper presents a framework for GPU-accelerated N-view triangulation in multi-view reconstruction that improves processing time and final reprojection error with respect to methods in the literature. The framework uses an algorithm based on optimizing an angular error-based L1cost function and it is shown how adaptive gradient descent can be applied for convergence. The triangulation algorithm is mapped onto the GPU and two approaches for parallelization are compared: one thread per track and one thread block per track. The better performing approach depends on the number of tracks and the lengths of the tracks in the dataset. Furthermore, the algorithm uses statistical sampling based on confidence levels to successfully reduce the quantity of feature track positions needed to triangulate an entire track. Sampling aids in load balancing for the GPU's SIMD architecture and for exploiting the GPU's memory hierarchy. When compared to a serial implementation, a typical performance increase of 3-4× can be achieved on a 4-core CPU. On a GPU, large track numbers are favorable and an increase of up to 40× can be achieved. Results on real and synthetic data prove that reprojection errors are similar to the best performing current triangulation methods but costing only a fraction of the computation time, allowing for efficient and accurate triangulation of large scenes.

This paper presents a framework for N-view triangulation of scene points, which improves processing time and final reprojection error with respect to standard methods, such as linear triangulation. The framework introduces an angular error-based cost function, which is robust to outliers and inexpensive to compute, and designed such that simple adaptive gradient descent can be applied for convergence. Our method also presents a statistical sampling component based on confidence levels, that reduces the number of rays to be used for triangulation of a given feature track. It is shown how the statistical component yields a meaningful yet much reduced set of representative rays for triangulation, and how the application of the cost function on the reduced sample can efficiently yield faster and more accurate solutions. Results are demonstrated on real and synthetic data, where it is proven to significantly increase the speed of triangulation and optimize reprojection error in most cases. This makes it especially attractive for efficient triangulation of large scenes given the speed and low memory requirements.