Computer Vision & Cultural Heritage Preservation
Multiscale Voxel Feature Fusion for Noisy Point Cloud Completion
This paper introduces a novel three-stage framework for high-fidelity point cloud completion, specifically designed for large-scale cultural heritage data often marred by occlusions and environmental noise. The framework integrates a Multistage Filtering Module for noise suppression, a Multiscale Voxel Feature Fusion Framework for detailed reconstruction, and a Curvature-guided Feature Enhancement Module to sharpen high-curvature areas. The method significantly outperforms existing approaches, demonstrating improved perceptual clarity and structural visibility, crucial for digital preservation.
Executive Impact at a Glance
The proposed Multiscale Voxel Feature Fusion Network (MVFF) delivers robust and precise point cloud completion for complex cultural heritage, significantly enhancing digital preservation and analysis capabilities.
0%
Performance Improvement on ShapeNet-55
0%
Performance Improvement on Cultural Heritage Dataset
0%
CD & HD Reduction (low noise) with MSF
0%
CD & HD Reduction (high noise) with MSF
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The MSF preprocesses raw scanned point clouds to reduce negative impacts of noise. It combines statistical outlier removal for significant outliers with guided filtering for local noise suppression, preserving geometric features. This two-stage approach ensures robust data cleaning without excessive smoothing, crucial for high-quality completion.
54.8%
CD & HD Reduction (low noise) with MSF
The MVFF addresses limitations of single-scale feature extraction by hierarchically extracting and fusing features at varying voxel granularities. It uses deconvolution to upsample coarse features and concatenate with finer ones, combined with a spatial attention mechanism. This enhances recovery of both global structures and local details, improving accuracy and detail preservation.
Enterprise Process Flow
Voxelize Input Cloud at Multiple Scales
→
Centroid-aware Feature Extraction (CFE)
→
Deconvolution & Concatenation
→
Spatial Attention Mechanism (SAM)
→
Fused Multiscale Features
The CFEM enhances reconstruction quality in high-curvature areas, such as edges, by guiding skeleton point prediction. It computes discrete curvature for predicted skeleton points and applies a learnable gating mechanism to reweight decoder features. This module ensures geometric consistency, preventing deformations and improving the reasonableness of the reconstructed point cloud.
8%
Performance Boost on ShapeNet-55 (CFEM)
The proposed three-stage framework synergistically combines noise reduction, multiscale feature extraction, and curvature-guided enhancement. This holistic approach addresses key challenges in large-scale point cloud completion, leading to superior accuracy, detail preservation, and robustness against noise and complex geometries compared to traditional and single-module deep learning methods.
| Feature | Existing Methods' Limitations | Proposed Method's Advantages |
|---|---|---|
| Detail Recovery |
|
|
| Noise Handling |
|
|
| Scalability & Efficiency |
|
|
The method was successfully applied to real-world laser-scanned data of Tamaki-jinja Shrine, a UNESCO World Heritage site. It effectively completed missing roof regions and improved structural visibility, enabling transparent visualization for internal analysis. This demonstrates the framework's practical utility for cultural heritage preservation and analysis, even with challenging, noisy, and incomplete datasets.
Tamaki-jinja Shrine Roof Completion
Laser scanning of the Tamaki-jinja Shrine roof often results in incomplete and noisy data due to occlusions and environmental interference. The proposed Multiscale Voxel Feature Fusion Network was applied to reconstruct these challenging regions.
Impact: The application successfully completed missing roof sections and enhanced overall structural visibility, enabling detailed transparent visualization. This significantly aids in the digital preservation and analysis of complex cultural heritage structures, demonstrating robustness to real-world data challenges.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by implementing advanced AI for point cloud processing.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Implementation Roadmap
A phased approach to integrate Multiscale Voxel Feature Fusion into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Data Preprocessing & Noise Mitigation
Implement the Multistage Filtering Module to clean raw point cloud data, removing outliers and suppressing noise while preserving geometric features. Validate the denoising quality on real-world scanned data to ensure a robust foundation for completion.
Phase 2: Multiscale Feature Learning Integration
Integrate the Multiscale Voxel Feature Fusion Framework for hierarchical feature extraction. Train and optimize the network to effectively capture both global structures and local details, ensuring robust feature representations across varying scales.
Phase 3: Curvature-Guided Reconstruction & Refinement
Incorporate the Curvature-guided Feature Enhancement Module to improve reconstruction fidelity in high-curvature regions. Fine-tune the model with reconstruction and geometry-consistent loss functions to achieve precise and geometrically reasonable point cloud completion.
Phase 4: Real-World Deployment & Transparent Visualization
Deploy the trained model on large-scale cultural heritage datasets. Integrate with transparent visualization techniques to enable enhanced structural perceptibility, validating the method's utility for digital preservation and analysis in practical scenarios.
Ready to Transform Your Point Cloud Data?
Book a personalized consultation to explore how Multiscale Voxel Feature Fusion can address your specific enterprise challenges.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat