EndoPBR: Material and Lighting Estimation for Photorealistic Surgical Simulations via Physically-based Rendering

TL;DR

EndoPBR introduces a differentiable rendering framework that estimates material and lighting in surgical scenes, enabling photorealistic view synthesis and improving 3D vision tasks in medical imaging.

Contribution

The paper presents a novel method for disentangling lighting and material properties in surgical scenes using a physically-based rendering approach, tailored for endoscopic images.

Findings

Produces photorealistic images at arbitrary viewpoints

Enables effective depth estimation via synthetic data

Achieves competitive view synthesis results

Abstract

The lack of labeled datasets in 3D vision for surgical scenes inhibits the development of robust 3D reconstruction algorithms in the medical domain. Despite the popularity of Neural Radiance Fields and 3D Gaussian Splatting in the general computer vision community, these systems have yet to find consistent success in surgical scenes due to challenges such as non-stationary lighting and non-Lambertian surfaces. As a result, the need for labeled surgical datasets continues to grow. In this work, we introduce a differentiable rendering framework for material and lighting estimation from endoscopic images and known geometry. Compared to previous approaches that model lighting and material jointly as radiance, we explicitly disentangle these scene properties for robust and photorealistic novel view synthesis. To disambiguate the training process, we formulate domain-specific properties inherent in surgical scenes. Specifically, we model the scene lighting as a simple spotlight and material properties as a bidirectional reflectance distribution function, parameterized by a neural network. By grounding color predictions in the rendering equation, we can generate photorealistic images at arbitrary camera poses. We evaluate our method with various sequences from the Colonoscopy 3D Video Dataset and show that our method produces competitive novel view synthesis results compared with other approaches. Furthermore, we demonstrate that synthetic data can be used to develop 3D vision algorithms by finetuning a depth estimation model with our rendered outputs. Overall, we see that the depth estimation performance is on par with fine-tuning with the original real images.