Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue (2112.08122v1)

Abstract: Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios. One widely adopted assumption of depth and ego-motion self-supervised learning is that the image brightness remains constant within nearby frames. Unfortunately, the endoscopic scene does not meet this assumption because there are severe brightness fluctuations induced by illumination variations, non-Lambertian reflections and interreflections during data collection, and these brightness fluctuations inevitably deteriorate the depth and ego-motion estimation accuracy. In this work, we introduce a novel concept referred to as appearance flow to address the brightness inconsistency problem. The appearance flow takes into consideration any variations in the brightness pattern and enables us to develop a generalized dynamic image constraint. Furthermore, we build a unified self-supervised framework to estimate monocular depth and ego-motion simultaneously in endoscopic scenes, which comprises a structure module, a motion module, an appearance module and a correspondence module, to accurately reconstruct the appearance and calibrate the image brightness. Extensive experiments are conducted on the SCARED dataset and EndoSLAM dataset, and the proposed unified framework exceeds other self-supervised approaches by a large margin. To validate our framework's generalization ability on different patients and cameras, we train our model on SCARED but test it on the SERV-CT and Hamlyn datasets without any fine-tuning, and the superior results reveal its strong generalization ability. Code will be available at: \url{https://github.com/ShuweiShao/AF-SfMLearner}.

• The paper introduces the innovative appearance flow concept to align brightness variations and enhance depth and motion estimation.
• It presents a unified self-supervised framework with four modules—structure, motion, appearance, and correspondence—to improve image calibration in endoscopic scenes.
• Experimental results on SCARED and EndoSLAM datasets demonstrate superior performance and robust generalization without the need for fine-tuning.

Overview of "Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue"

The paper presented by Shuwei Shao et al. focuses on a novel approach for monocular depth and ego-motion estimation specifically designed for endoscopic scenes, utilizing self-supervised learning methodologies. The primary challenge addressed in this work is the severe brightness inconsistency found in endoscopic videos, which is an obstacle for conventional depth and motion estimation methods that assume constant brightness across frames. This assumption is largely violated due to the complex illumination changes inherent in endoscopic environments.

Key Contributions

1. Appearance Flow Concept: The authors introduce an innovative concept termed "appearance flow," which effectively captures variations in brightness between frames. This contrasts with traditional methods relying solely on geometric transformations, providing a framework that integrates both geometric and radiometric transformations.
2. Unified Self-Supervised Framework: The work proposes a unified framework composed of four modules: structure, motion, appearance, and correspondence. Each module plays a crucial role in accurately estimating depth and calibrating image brightness. The framework leverages the appearance module to predict appearance flows, thereby aligning brightness and improving estimation accuracy.
3. Enhanced Generalization and Robustness: Extensive experiments conducted on datasets such as SCARED and EndoSLAM demonstrate the framework's superior performance in comparison to existing self-supervised techniques. Notably, the framework shows remarkable generalization capabilities, tested across datasets without fine-tuning, indicating its robustness to different patient data and camera systems.
4. Numerical Results: The proposed framework significantly surpasses comparative methods in both depth and ego-motion estimation accuracy. It achieves notable performance on the SCARED dataset, with metrics such as Absolute Relative Difference (Abs Rel) and Root Mean Squared Error (RMSE) being better than those of previously established methods.

Implications and Speculations

The introduction of appearance flow has profound implications in computer vision, especially in medical applications such as endoscopic surgery. By addressing the issue of brightness fluctuations in endoscopic scenes, the framework paves the way for more reliable depth and motion estimation useful for augmented reality-based navigation systems in minimally invasive surgeries. Furthermore, the approach could be extended to other environments where brightness constancy doesn't hold, enhancing applications in autonomous navigation under complex lighting conditions.

In speculating future developments, appearance flow's application might evolve with advancements in neural network architectures and computation, potentially allowing real-time processing and broader applicability outside of healthcare, such as in autonomous vehicles operating under adverse weather conditions. Additionally, incorporating multi-view inputs might further mitigate issues like oversaturated regions, enhancing reconstruction fidelity.

In summary, this paper introduces a robust and adaptable framework that effectively addresses brightness inconsistency in endoscopic video data, contributing significantly to the field of self-supervised depth and motion estimation. This work holds promise for expansion into other domains requiring precise visual odometry under challenging lighting conditions.