C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy

• URL: http://arxiv.org/abs/2206.01961v1
• Date: Sat, 4 Jun 2022 10:38:19 GMT
• Title: C$^3$Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy
• Authors: Erez Posner and Adi Zholkover and Netanel Frank and Moshe Bouhnik
• Abstract summary: 3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined surfaces remains an unsolved problem. Recent methods demonstrate compelling results, but suffer from: (1) frangible frame-to-frame (or frame-to-model) pose estimation resulting in many tracking failures; or (2) rely on point-based representations at the cost of scan quality. We propose a novel reconstruction framework that addresses these issues end to end, which result in both quantitatively and qualitatively accurate and robust 3D colon reconstruction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 3D colon reconstruction from Optical Colonoscopy (OC) to detect non-examined surfaces remains an unsolved problem. The challenges arise from the nature of optical colonoscopy data, characterized by highly reflective low-texture surfaces, drastic illumination changes and frequent tracking loss. Recent methods demonstrate compelling results, but suffer from: (1) frangible frame-to-frame (or frame-to-model) pose estimation resulting in many tracking failures; or (2) rely on point-based representations at the cost of scan quality. In this paper, we propose a novel reconstruction framework that addresses these issues end to end, which result in both quantitatively and qualitatively accurate and robust 3D colon reconstruction. Our SLAM approach, which employs correspondences based on contrastive deep features, and deep consistent depth maps, estimates globally optimized poses, is able to recover from frequent tracking failures, and estimates a global consistent 3D model; all within a single framework. We perform an extensive experimental evaluation on multiple synthetic and real colonoscopy videos, showing high-quality results and comparisons against relevant baselines.

Related papers

• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices. Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation [67.22294293695255]
  We propose a novel reconstruction pipeline with a bi-directional adaptation architecture named ToDER to get precise depth estimations. Experimental results demonstrate that our approach can precisely predict depth maps in both realistic and synthetic colonoscopy videos.
  arXiv  Detail & Related papers  (2024-07-23T14:24:26Z)
• EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting [39.60431471170721]
  3D reconstruction of biological tissues from a collection of endoscopic images is a key to unlock various important downstream surgical applications with 3D capabilities. Existing methods employ various advanced neural rendering techniques for view synthesis, but they often struggle to recover accurate 3D representations when only sparse observations are available. We propose a framework leveraging the prior knowledge from multiple foundation models during the reconstruction process, dubbed as textitEndoSparse.
  arXiv  Detail & Related papers  (2024-07-01T07:24:09Z)
• Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy [0.2995885872626565]
  We develop a novel multi-task learning (MTL) approach with a shared encoder and two decoders, namely a surface normal decoder and a depth estimator. We demonstrate an improvement of 14.17% on relative error and 10.4% on $delta_1$ accuracy over the most accurate baseline state-of-the-art BTS approach.
  arXiv  Detail & Related papers  (2023-11-30T16:13:17Z)
• A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video [8.32570164101507]
  We perform a quantitative analysis of a self-supervised approach for sinus reconstruction using endoscopic sequences and optical tracking. Our results show that the generated reconstructions are in high agreement with the anatomy, yielding an average point-to-mesh error of 0.91 mm. We identify that pose and depth estimation inaccuracies contribute equally to this error and that locally consistent sequences with shorter trajectories generate more accurate reconstructions.
  arXiv  Detail & Related papers  (2023-10-22T17:11:40Z)
• OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images [59.51657161097337]
  OOD-CV-v2 is a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods.
  arXiv  Detail & Related papers  (2023-04-17T20:39:25Z)
• On Robust Cross-View Consistency in Self-Supervised Monocular Depth Estimation [56.97699793236174]
  We study two kinds of robust cross-view consistency in this paper. We exploit the temporal coherence in both depth feature space and 3D voxel space for self-supervised monocular depth estimation. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-09-19T03:46:13Z)
• ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction [27.793186578742088]
  In this work we have designed a set of training losses to deal with the special challenges of colonoscopy data. With the training losses powerful enough, our self-supervised framework named ColDE is able to produce better depth maps of colonoscopy data.
  arXiv  Detail & Related papers  (2021-11-19T04:44:27Z)
• Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance [86.05191217004415]
  Multi-view reconstruction of texture-less objects with unknown surface reflectance is a challenging task. This paper proposes a simple and robust solution to this problem based on a co-light scanner.
  arXiv  Detail & Related papers  (2021-05-25T01:28:54Z)
• Secrets of 3D Implicit Object Shape Reconstruction in the Wild [92.5554695397653]
  Reconstructing high-fidelity 3D objects from sparse, partial observation is crucial for various applications in computer vision, robotics, and graphics. Recent neural implicit modeling methods show promising results on synthetic or dense datasets. But, they perform poorly on real-world data that is sparse and noisy. This paper analyzes the root cause of such deficient performance of a popular neural implicit model.
  arXiv  Detail & Related papers  (2021-01-18T03:24:48Z)

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.