CaLoRA-Stereo: Robust Stereo Endoscopic Depth Estimation Network Via Camera-Aware LoRA and Dual-View Geometry

Abstract

Stereo depth estimation has drawn widespread attention from the robotics and vision community due to its broad applications such as 3D reconstruction. Recently, stereo matching foundation models have made significant progress by being trained on the large-scale datasets containing natural images. However, directly leveraging these pretrained large models to minimally invasive surgery still remains challenging due to domain shifts in aspects of specular highlights and low-texture tissue. In this paper, we propose a parameter-efficient adaptation framework to address this gap. Specifically, we introduce Camera-Aware LoRA for fine-tuning FoundationStereo, using a camera-aware scaling gate computed from focal length and baseline to address intraoperative intrinsics drift arising from instrument self-heating and other thermal effects. We further develop a geometric consistency constraint and a spectral alignment regularizer that enforce cross-view depth agreement. Extensive experiments on the SCARED and Hamlyn datasets indicate that the proposed method achieves state-of-the-art performance. Notably, CaLoRA is easy to integrate into standard fine-tuning pipelines, requiring no backbone changes and only a small number of trainable parameters.