Sonar-neus:voxel-based efficient neural implicit surface reconstruction for forward-looking sonar
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a faster method for 3D ocean mapping using forward-looking sonar (FLS). The new voxel-based approach significantly speeds up dense 3D reconstruction, making ocean exploration more efficient.
Area Of Science
- Robotics and Autonomous Systems
- Oceanography and Marine Technology
- Computer Vision and Graphics
Background
- Dense 3D reconstruction with forward-looking sonar (FLS) is crucial for ocean exploration.
- Current neural radiance field methods are too slow for real-world FLS 3D reconstruction.
- Existing methods suffer from slow training (hours) and inefficient sampling strategies.
Purpose Of The Study
- To develop a significantly faster and more efficient dense 3D reconstruction method for FLS data.
- To overcome the limitations of slow training and inefficient sampling in current neural radiance field approaches.
- To improve the quality and speed of 3D reconstruction for underwater environments.
Main Methods
- Proposed a voxel-based neural implicit surface reconstruction approach for FLS.
- Replaced MLPs with geometry (SDF voxel grid) and appearance (feature voxel grid) representations.
- Introduced hierarchical sampling along the elevation arc and SDF Gaussian convolution for noise reduction.
Main Results
- Achieved dense 3D reconstruction quality comparable to state-of-the-art methods.
- Reduced training time from 4 hours to just 10 minutes.
- Demonstrated superior performance over existing unsupervised dense FLS reconstruction techniques.
Conclusions
- The proposed voxel-based method offers a substantial speed improvement for FLS 3D reconstruction.
- This advancement significantly enhances the feasibility of dense 3D reconstruction for real-world ocean exploration.
- The approach provides a more efficient and higher-quality solution for underwater mapping.
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