Gastroendoscopy View Synthesis: A New Real Dataset and Evaluation
This study addresses the lack of realistic evaluation datasets for novel view synthesis in gastroscopy, which has hindered progress in applications such as field-of-view expansion and digital twins. To bridge this gap, we introduce and publicly release GastroNVS—the first real-world dataset for novel view synthesis in gastroscopy—comprising synchronously captured endoscopic images, camera poses, and 3D point clouds. Leveraging this dataset, we conduct a systematic benchmark of multiple 3D Gaussian Splatting (3DGS) approaches, revealing their respective strengths and limitations in the complex gastric environment. Our work establishes a high-quality benchmark that fills a critical void in the field and provides a foundational resource for advancing algorithm development and clinical translation.