Enhanced Edge Highlighting for 3D Scanned Point Clouds Using Dual Edge Extraction

Tavana Mert^*
*Correspondence: Tavana Mert, Department of Computer Engineering, King Saud University, Riyadh, Saudi Arabia, Email:

Introduction

The visualization of 3D scanned point clouds is a crucial aspect of many applications, including computer graphics, engineering, and cultural heritage preservation. One of the key challenges in rendering point cloud data is effectively highlighting the edges of objects within the scan, as edges provide essential structural and geometric information. Traditional visualization methods often struggle with clarity, especially in high-density point clouds, leading to difficulties in perceiving object boundaries. To address this issue, a dual 3D edge extraction approach can be employed to enhance edge visibility, improving the overall clarity and interpretability of point cloud data. Edge highlighting in point clouds is complex due to the unstructured nature of the data. Unlike structured meshes, which consist of predefined connectivity, point clouds are collections of discrete points without explicit connectivity information. This lack of structure necessitates the development of specialized edge detection and enhancement techniques. The dual 3D edge extraction method involves identifying edges using two complementary approaches: geometricbased edge detection and feature-based edge extraction. By combining these methods, a more robust and visually appealing edge-enhancement technique can be achieved.

Description

Geometric-based edge detection relies on analyzing the local curvature and surface normal variations in the point cloud. High curvature regions typically correspond to sharp edges, corners, and transitions between different surfaces. One common technique involves estimating surface normals for each point and computing the angular difference between neighboring normals. When the difference exceeds a certain threshold, the point is classified as part of an edge. This method effectively captures geometric discontinuities, making it useful for identifying sharp object boundaries. Feature-based edge extraction complements geometric techniques by leveraging additional point cloud attributes such as intensity values, color variations, or density changes. Many 3D scanning systems, including LiDAR and structured light scanners, capture intensity information alongside spatial coordinates. Significant variations in intensity or color often correspond to material changes or surface boundaries, which can serve as additional cues for edge detection. By integrating feature-based analysis, edge extraction can be refined to better highlight object contours and material transitions. Once edges are extracted using both methods, a fusion strategy is applied to combine the results into a unified visualization. A weighting mechanism is introduced to balance the contributions of geometric and feature-based edges, ensuring that the final visualization emphasizes prominent edges while suppressing noise. This fusion process enhances the perception of object boundaries, making it easier to interpret complex structures within the point cloud [1].

To improve the rendering of highlighted edges, specialized visualization techniques such as color encoding, transparency adjustments, and point size modulation can be applied. Color encoding assigns different colors to edges based on their significance, providing an intuitive way to differentiate between major and minor edges. Transparency adjustments allow non-edge points to fade into the background, reducing visual clutter and directing attention to the highlighted edges. Point size modulation dynamically adjusts the size of rendered points based on their edge classification, further enhancing visibility and depth perception. Performance optimization is an important consideration when implementing dual edge extraction in real-time applications. Processing large point clouds can be computationally intensive, requiring efficient algorithms for normal estimation, curvature analysis, and feature extraction. Parallel processing techniques, such as GPU acceleration and multi-threading, can significantly improve performance. Additionally, adaptive edge extraction strategies can be employed to prioritize high-curvature regions while reducing computations in flat areas, optimizing processing speed without sacrificing edge quality. The effectiveness of the dual edge extraction method can be evaluated through both qualitative and quantitative assessments. Qualitative evaluations involve visual inspections of edge-enhanced point cloud renderings, comparing them against traditional methods to assess clarity and interpretability [2,3].

Quantitative metrics, such as edge accuracy and completeness, can be computed by comparing detected edges with ground truth data obtained from manually annotated scans or high-resolution mesh models. User studies can also be conducted to gather feedback from professionals in fields such as architecture, engineering, and cultural heritage documentation to assess the practical benefits of enhanced edge visualization. Applications of this technique span multiple domains. In industrial inspection, edge-enhanced visualization helps engineers analyze manufactured components, identifying defects and inconsistencies in production. In cultural heritage preservation, the method aids in documenting and analyzing historical artifacts and architectural structures, ensuring that fine details are captured accurately. In robotics and autonomous navigation, improved edge detection enhances scene understanding, allowing robots to navigate more effectively in complex environments. Additionally, in medical imaging, enhanced edge visualization can be applied to point cloud representations of anatomical structures, improving diagnostic and surgical planning capabilities. Future developments in edge highlighting for point clouds could incorporate machine learning techniques to further refine edge detection and enhancement. Deep learning models trained on large datasets of point cloud edges could learn to distinguish between significant and insignificant edges more effectively than handcrafted methods [4,5].

Conclusion

Additionally, integrating semantic segmentation could allow for contextaware edge highlighting, differentiating between different types of edges based on object categories and material properties. Improved data fusion techniques, incorporating multispectral or hyperspectral imaging data, could further enhance the ability to detect edges in complex scenarios. The dual 3D edge extraction approach provides a robust solution for enhancing edge visibility in 3D scanned point clouds. By combining geometric-based and feature-based edge detection methods, the technique effectively highlights object boundaries, improving the clarity and interpretability of point cloud data. With applications in engineering, cultural heritage, robotics, and medical imaging, this method has the potential to significantly impact various industries. Continued advancements in computational efficiency, visualization techniques, and machine learning integration will further enhance the capabilities and applicability of edge-enhanced point cloud visualization in the future.

Acknowledgment

None.

Conflict of Interest

None.

References

1. Everts, Maarten H., Henk Bekker, Jos BTM Roerdink and Tobias Isenberg. "Depth-dependent halos: Illustrative rendering of dense line data." IEEE Trans Vis Comput Graph 15 (2009): 1299-1306.

   Google Scholar, Crossref, Indexed at

2. Wenger, Andreas, Daniel F. Keefe, Song Zhang and David H. Laidlaw. "Interactive volume rendering of thin thread structures within multivalued scientific data sets." IEEE Trans Vis Comput Graph 10 (2004): 664-672.

   Google Scholar, Crossref, Indexed at

3. Bruckner, Stefan and Eduard Gröller. "Enhancing depth-perception with flexible volumetric halos." IEEE Trans Vis Comput Graph 13 (2007): 1344-1351.

   Google Scholar, Crossref, Indexed at

4. Pan, Jiao, Liang Li, Hiroshi Yamaguchi and Kyoko Hasegawa, et al. "3D reconstruction of Borobudur reliefs from 2D monocular photographs based on soft-edge enhanced deep learning." ISPRS J Photogramm Remote Sens 183 (2022): 439-450.

   Google Scholar, Crossref, Indexed at

5. Rheingans, Penny and David Ebert. "Volume illustration: Nonphotorealistic rendering of volume models." IEEE Trans Vis Comput Graph 7 (2001): 253-264.

   Google Scholar, Crossref, Indexed at