Monocular Pipeline 3D Reconstruction with Learned Depth Prediction
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Solution Overview
Problem
Current methods for three-dimensional reconstruction of urban underground drainage pipelines face challenges due to complex internal environments, unique geographical locations, and limited data utilization, leading to inaccurate positioning, low efficiency, and difficulty in reconstructing low-textured areas.
Innovation Solution
A monocular video-based three-dimensional reconstruction and depth prediction method and system that employs Zhang's calibration method for depth camera calibration, uses COLMAP for feature extraction and incremental reconstruction, and trains Fast-MVSNet and PatchMatchNet networks to achieve accurate three-dimensional reconstruction and depth prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SfM method is used for monocular vision-based three-dimensional reconstruction, then the reconstruction process can be implemented, but accumulation of errors causes drift and low reconstruction quality
Solution Approach 1:
The patent replaces the traditional mechanical SfM reconstruction process with a deep learning-based neural network system. The MVSNet architecture uses convolutional neural networks to predict depth maps and reconstruct 3D points, substituting the iterative geometric computation of SfM with learned visual features, thereby eliminating accumulation errors and drift while improving both accuracy and stability
Solution Approach 2:
The patent changes the fundamental parameters of the reconstruction system by introducing multiple view images and depth map predictions as additional inputs. Instead of relying solely on single-view geometry, the system uses multi-view stereo constraints and learned depth priors to stabilize reconstruction, transforming the problem from 2D-to-3D lifting to multi-constraint joint optimization
2Measurement precision
If general network architecture (MVSNet) is used for three-dimensional reconstruction, then reconstruction integrity and accuracy improve, but dependence on large video memory and memory leads to high hardware requirements
Solution Approach 1:
The patent segments the reconstruction process into distinct modular components: feature extraction from multiple views, depth map prediction through separate neural network branches, cost volume construction, and 3D point generation. This modular architecture allows selective processing and reduces memory bandwidth requirements compared to the monolithic MVSNet approach
Solution Approach 2:
The patent implements progressive refinement where the network first generates a coarse depth map, then iteratively refines it through multiple passes with decreasing resolution. This partial processing approach processes only necessary details at each scale, reducing overall video memory requirements while maintaining final reconstruction accuracy
3Measurement precision
If deep learning methods are used for three-dimensional reconstruction, then reconstruction integrity improves, but algorithms are not effective in low-textured and weak-light pipeline environments
Solution Approach 1:
The patent enhances the network's universality by training it on diverse datasets including low-textured and weak-light scenarios. The MVSNet is designed to handle multiple lighting conditions and texture variations through data augmentation and multi-scale feature extraction, making it adaptable to pipeline environments that general-purpose algorithms struggle with
Solution Approach 2:
The patent introduces intermediate depth map predictions and cost volumes as mediators between input images and final 3D reconstruction. These intermediates capture structural information that is more robust to lighting and texture variations, allowing the system to bridge the gap between degraded pipeline images and accurate 3D reconstruction
4Measurement precision
If incremental reconstruction is performed on pipeline scene image sequences, then camera external parameters can be obtained, but the process is time-consuming and low efficiency
Solution Approach 1:
The patent performs preliminary feature extraction and matching on all image pairs before the incremental reconstruction process. By pre-computing feature descriptors and matching relationships, the system avoids repeated computation during the reconstruction phases, significantly accelerating the overall process while maintaining parameter accuracy
Solution Approach 2:
The patent implements continuous processing where feature extraction, matching, and reconstruction operations overlap across multiple image sequences. The system maintains running accumulations of 3D points and camera parameters, avoiding restarts and reprocessing, thereby achieving continuous high-speed reconstruction of pipeline scenes
Data Source
AI summary
A monocular video-based three-dimensional reconstruction and depth prediction method and system for a pipeline are provided, relating to the technical field of deep learning and image processing. The method includes the following steps: performing feature extraction and matching and incremental reconstruction on collected image sequences of different pipeline scenes based on the COLMAP method to obtain a camera external parameter matrix corresponding to each image, and constructing a pipeline three-dimensional reconstruction dataset; training the Fast-MVSNet network and the PatchMatchNet network using the existing public datasets, and selecting an optimal network model from multiple trained network models for three-dimensional reconstruction and depth prediction of an actual pipeline scene. A holistic framework from a monocular video to a three-dimensional scene is constructed, which can effectively achieve three-dimensional reconstruction and depth prediction of a drainage pipeline and intuitively display internal structure information of the pipeline.