ArticleSensors (Basel, Switzerland)2026
Latent Code Predictor for Accelerating Disparity Estimation in Stereo-Endoscopic Surface Reconstruction.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Disparity estimation from stereo-endoscopic images is critical for 3D reconstruction in minimally invasive surgery (MIS). However, surgical environments have inherent interference factors including soft tissue deformation, motion blur, and photometric inconsistency. Currently, self-supervised generative networks such as StyleGAN offer an alternative method, but their reliance on iterative latent optimization leads to high computational latency and limits practical deployment. In this work, we propose a temporal latent prediction method to accelerate this optimization process. Instead of designing a brand new generator, our framework learns to predict an optimized initial latent vector, thereby reducing the number of optimization steps and per-frame inference time. Crucially, this prediction-guided mechanism does not alter the architecture or inference logic of the generator, ensuring the fidelity of reconstruction is comparable to that of the original method. Experiments on
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