3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint

• URL: http://arxiv.org/abs/2006.10383v2
• Date: Fri, 3 Jul 2020 04:52:21 GMT
• Title: 3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint
• Authors: Sho kagami, Hajime Taira, Naoyuki Miyashita, Akihiko Torii, Masatoshi Okutomi
• Abstract summary: We propose a 3D pipe reconstruction system using sequential images captured by a monocular endoscopic camera. Our work extends a state-of-the-art incremental Structure-from-Motion (SfM) method to incorporate prior constraints given by the target shape into bundle adjustment. In the experiments, we show that the proposed system enables more accurate and robust pipe mapping from a monocular camera.
• Score: 13.656321204701669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pipe inspection is a critical task for many industries and infrastructure of a city. The 3D information of a pipe can be used for revealing the deformation of the pipe surface and position of the camera during the inspection. In this paper, we propose a 3D pipe reconstruction system using sequential images captured by a monocular endoscopic camera. Our work extends a state-of-the-art incremental Structure-from-Motion (SfM) method to incorporate prior constraints given by the target shape into bundle adjustment (BA). Using this constraint, we can minimize the scale-drift that is the general problem in SfM. Moreover, our method can reconstruct a pipe network composed of multiple parts including straight pipes, elbows, and tees. In the experiments, we show that the proposed system enables more accurate and robust pipe mapping from a monocular camera in comparison with existing state-of-the-art methods.

Related papers

• OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
  We propose an OccNeRF method for training occupancy networks without 3D supervision. We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
  arXiv  Detail & Related papers  (2023-12-14T18:58:52Z)
• 3D Reconstruction of Spherical Images based on Incremental Structure from Motion [2.6432771146480283]
  This study investigates the algorithms for the relative orientation using spherical correspondences, absolute orientation using 3D correspondences between scene and spherical points, and the cost functions for BA (bundle adjustment) optimization. An incremental SfM (Structure from Motion) workflow has been proposed for spherical images using the above-mentioned algorithms.
  arXiv  Detail & Related papers  (2023-06-22T09:49:28Z)
• Registering Neural Radiance Fields as 3D Density Images [55.64859832225061]
  We propose to use universal pre-trained neural networks that can be trained and tested on different scenes. We demonstrate that our method, as a global approach, can effectively register NeRF models.
  arXiv  Detail & Related papers  (2023-05-22T09:08:46Z)
• TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering [54.35405028643051]
  We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps. We adopt the neural implicit surface reconstruction method, which allows for high-quality mesh.
  arXiv  Detail & Related papers  (2023-03-27T10:07:52Z)
• System for 3D Acquisition and 3D Reconstruction using Structured Light for Sewer Line Inspection [1.5854438418597576]
  We introduce an innovative system based on single-shot structured light modules that facilitates the detection and classification of spatial defects. This system creates highly accurate 3D measurements with sub-millimeter resolution of pipe surfaces and fuses them into a holistic 3D model. The benefit of such a holistic 3D model is twofold: on the one hand, it facilitates the accurate manual sewer pipe assessment, on the other, it simplifies the detection of defects in downstream automatic systems.
  arXiv  Detail & Related papers  (2023-03-06T09:10:55Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• Unsupervised Learning of Depth, Camera Pose and Optical Flow from Monocular Video [3.838877984537827]
  DFPNet -- an unsupervised, joint learning system for monocular Depth, Optical Flow and egomotion estimation. We leverage this fact to jointly train all the three components in an end-to-end manner. We are able to reduce the model size to less than 5% (8.4M parameters) of state-of-the-art DFP models.
  arXiv  Detail & Related papers  (2022-05-19T19:47:41Z)
• Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation [111.89519571205778]
  In this work, we propose an alternative domain-adaptive approach to depth estimation. Our novel two-step structure first trains a depth estimation network with labeled synthetic images in a supervised manner. The results of our experiments show that the proposed method improves the network's performance on real images by a considerable margin.
  arXiv  Detail & Related papers  (2021-09-24T08:11:34Z)
• VIO-Aided Structure from Motion Under Challenging Environments [12.111638631118026]
  We present a robust and efficient Structure from Motion pipeline for accurate 3D reconstruction under challenging environments. Specifically, we propose a geometric verification method to filter out mismatches by considering the prior geometric configuration of candidate image pairs.
  arXiv  Detail & Related papers  (2021-01-24T06:35:52Z)
• Transferable Active Grasping and Real Embodied Dataset [48.887567134129306]
  We show how to search for feasible viewpoints for grasping by the use of hand-mounted RGB-D cameras. A practical 3-stage transferable active grasping pipeline is developed, that is adaptive to unseen clutter scenes. In our pipeline, we propose a novel mask-guided reward to overcome the sparse reward issue in grasping and ensure category-irrelevant behavior.
  arXiv  Detail & Related papers  (2020-04-28T08:15:35Z)

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.