Gastroendoscopy View Synthesis: A New Real Dataset and Evaluation

• URL: http://arxiv.org/abs/2606.25427v1
• Date: Wed, 24 Jun 2026 05:36:49 GMT
• Title: Gastroendoscopy View Synthesis: A New Real Dataset and Evaluation
• Abstract summary: Novel view synthesis (NVS) is an active research topic in computer vision, owing to the success of neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) methods.<n>We present the first real gastroscopy dataset for NVS, namely the GastroNVS dataset, which contains a set of gastroscopic images, camera poses, and a point cloud for real gastroendoscopy inspection.
• Score: 17.312564012518653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Novel view synthesis (NVS) is an active research topic in computer vision, owing to the success of neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) methods. While NVS opens the door to potential applications in gastroendoscopy, such as extending the field of view of endoscopic images and enabling digital twins for 3D archiving and endoscopist manipulation training, the dataset is insufficient to evaluate NVS for gastroendoscopy. In this paper, we present the first real gastroscopy dataset for NVS, namely the GastroNVS dataset, which contains a set of gastroscopic images, camera poses, and a point cloud for real gastroendoscopy inspection. To assess the suitability of the GastroNVS dataset, we evaluate several 3DGS methods and discuss the challenges for future development. The dataset is available on request from our project page.

Related papers

• NeRFscopy: Neural Radiance Fields for in-vivo Time-Varying Tissues from Endoscopy [18.633528239379483]
  We introduce NeRFscopy, a self-supervised pipeline for novel view synthesis and 3D reconstruction of deformable endoscopic tissues from a monocular video.<n>NeRFscopy achieves accurate results in terms of novel view synthesis, outperforming competing methods across various challenging endoscopy scenes.
  arXiv  Detail & Related papers  (2026-02-17T18:05:23Z)
• Generative Enhancement for 3D Medical Images [74.17066529847546]
  We propose GEM-3D, a novel generative approach to the synthesis of 3D medical images. Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask. By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images.
  arXiv  Detail & Related papers  (2024-03-19T15:57:04Z)
• NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food Scene Dataset for Dietary Intake Estimation [71.22646949733833]
  We introduce NutritionVerse- Synth (NV- Synth), a large-scale synthetic food image dataset. NV- Synth contains 84,984 photorealistic meal images rendered from 7,082 dynamically plated 3D scenes. As the largest open-source synthetic food dataset, NV- Synth highlights the value of physics-based simulations.
  arXiv  Detail & Related papers  (2023-12-11T08:15:49Z)
• GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection [6.231109933741383]
  This dataset includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings from the GI tract. It was annotated and verified by experienced GI endoscopists. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification.
  arXiv  Detail & Related papers  (2023-07-16T19:36:03Z)
• NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations [64.95582364215548]
  NAVI is a new dataset of category-agnostic image collections with high-quality 3D scans and per-image 2D-3D alignments. These 2D-3D alignments allow us to extract accurate derivative annotations such as dense pixel correspondences, depth and segmentation maps.
  arXiv  Detail & Related papers  (2023-06-15T13:11:30Z)
• EndoMapper dataset of complete calibrated endoscopy procedures [8.577980383972005]
  This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice. Data will be used to build a 3D mapping and localization systems that can perform special task like, for example, detect blind zones during exploration.
  arXiv  Detail & Related papers  (2022-04-29T17:10:01Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction [8.448866668577946]
  We present a stereo-endoscopic reconstruction validation dataset based on CT (SERV-CT) The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images.
  arXiv  Detail & Related papers  (2020-12-22T01:28:30Z)
• Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern [49.240017254888336]
  Photoacoustic tomography (PAT) is a novel imaging technique that can resolve both morphological and functional tissue properties. A current drawback is the limited field-of-view provided by the conventionally applied 2D probes. We present a novel approach to 3D reconstruction of PAT data that does not require an external tracking system.
  arXiv  Detail & Related papers  (2020-11-10T09:27:56Z)
• Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples [74.92488215859991]
  We propose a novel 3D-based coarse-to-fine framework to efficiently tackle these challenges. The proposed 3D-based framework outperforms their 2D counterparts by a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on three datasets, the NIH pancreas dataset, the JHMI pancreas dataset and the JHMI pathological cyst dataset.
  arXiv  Detail & Related papers  (2020-10-29T15:39:19Z)
• EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner [10.341552258136572]
  We introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs. A synthetic capsule endoscopy frame with both depth and pose annotations is included to facilitate the study of simulation-to-real transfer learning algorithms. We propound Endo-SfMLearner, an unsupervised monocular depth and pose estimation method.
  arXiv  Detail & Related papers  (2020-06-30T10:43:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.