Texturing Endoscopic 3D Stomach via Neural Radiance Field Under Uneven Lighting

Texture generation is crucial for endoscopic 3D reconstruction, as it provides essential visual information for computer-assisted medical diagnosis and surgical procedures. Previous mapping-based methods for texturing 3D stomach depend on high-quality mesh structures for accurate camera view selection. However, it is challenging to obtain high-quality mesh structures in endoscopic 3D reconstruction. To eliminate this dependency, we propose an alternative texture generation method that extracts texture directly from a neural radiance field, removing the need for camera view selection. Furthermore, since endoscopic images often suffer from uneven lighting including local low light and overexposure, we develop a weight mechanism to guide our model in prioritizing the learning of pixels that clearly depict the stomach wall. Experimental results demonstrate that our method is more robust than previous approaches in texturing 3D stomach models and effectively mitigates lighting artifacts, thereby producing high-fidelity textures that are crucial for downstream tasks.