A monocular endoscopy framework fuses Depth Pro and Depth Anything depth with RAFT-LPIPS temporal refinement and dog-leg pose optimization to reconstruct tissue surfaces and camera trajectories.
Predicting Performance of SLAM Algorithms
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abstract
Among the abilities that autonomous mobile robots should exhibit, map building and localization are definitely recognized as fundamental. Consequently, countless algorithms for solving the Simultaneous Localization And Mapping (SLAM) problem have been proposed. Currently, their evaluation is performed ex-post, according to outcomes obtained when running the algorithms on data collected by robots in real or simulated environments. In this paper, we present a novel method that allows the ex-ante prediction of the performance of a SLAM algorithm in an unseen environment, before it is actually run. Our method collects the performance of a SLAM algorithm in a number of simulated environments, builds a model that represents the relationship between the observed performance and some geometrical features of the environments, and exploits such model to predict the performance of the algorithm in an unseen environment starting from its features.
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Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction
A monocular endoscopy framework fuses Depth Pro and Depth Anything depth with RAFT-LPIPS temporal refinement and dog-leg pose optimization to reconstruct tissue surfaces and camera trajectories.