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1. Direct medical image to simulation using auto-segmentation and point cloud-based CFD

   https://doi.org/10.3934/acse.2025006  

   Corpuz, Ashton M; Jaiswal, Monu; Du, Pan; Ramachandra, Abhay B; Wang, Jian-Xun; Hsu, Ming-Chen (March 2025, Advances in Computational Science and Engineering)

   Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide. Computational medicine and digital twins hold promise in mitigating the impact and prevalence of CVD. Recent advances in image-based computational methods have enabled the quantification of functional and biologically important metrics that would otherwise be difficult to obtain from the standard of care. However, significant challenges remain due to the manual/semi-automated nature of the processes and the domain expertise required to perform them. This paper addresses these challenges by proposing a novel framework that builds on our recently developed direct point cloud-to-CFD approach using immersogeometric analysis. The proposed method leverages advanced auto-segmentation techniques to extract medically relevant geometries as point clouds, which are then directly used for CFD simulations. The framework is validated using benchmark flow problems with analytical and computational solutions and is subsequently applied to patient-specific images to demonstrate its capabilities. The results highlight the method's ability to facilitate rapid CFD simulations directly on point clouds derived from patient scans, underscoring its potential to accelerate the image-to-simulation pipeline and enable the tractability of cardiovascular digital twins. 

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2. Centroid Distance Keypoint Detector for Colored Point Clouds

   https://doi.org/10.1109/WACV56688.2023.00125  

   Teng, H.; Chatziparaschis, D.; Kan, X.; Roy-Chowdhury, A.; Karydis, K. (January 2023, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))

   Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) keypoint detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art keypoint detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of-the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector. 

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3. Point Cloud Attribute Compression Via Chroma Subsampling

   https://doi.org/10.1109/ICASSP43922.2022.9746352  

   Sridhara, Shashank N.; Pavez, Eduardo; Ortega, Antonio; Watanabe, Ryosuke; Nonaka, Keisuke (May 2022, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   We introduce chroma subsampling for 3D point cloud attribute compression by proposing a novel technique to sample points irregularly placed in 3D space. While most current video compression standards use chroma subsampling, these chroma subsampling methods cannot be directly applied to 3D point clouds, given their irregularity and sparsity. In this work, we develop a framework to incorporate chroma subsampling into geometry-based point cloud encoders, such as region adaptive hierarchical transform (RAHT) and region adaptive graph Fourier transform (RAGFT). We propose different sampling patterns on a regular 3D grid to sample the points at different rates. We use a simple graph-based nearest neighbor interpolation technique to reconstruct the full resolution point cloud at the decoder end. Experimental results demonstrate that our proposed method provides significant coding gains with negligible impact on the reconstruction quality. For some sequences, we observe a bitrate reduction of 10-15% under the Bjontegaard metric. More generally, perceptual masking makes it possible to achieve larger bitrate reductions without visible changes in quality. 

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4. 3D Reconstruction with Fast Dipole Sums

   https://doi.org/10.1145/3687914  

   Chen, Hanyu; Miller, Bailey; Gkioulekas, Ioannis (December 2024, ACM Transactions on Graphics)

   We introduce a method for high-quality 3D reconstruction from multi-view images. Our method uses a new point-based representation, the regularized dipole sum, which generalizes the winding number to allow for interpolation of per-point attributes in point clouds with noisy or outlier points. Using regularized dipole sums, we represent implicit geometry and radiance fields as per-point attributes of a dense point cloud, which we initialize from structure from motion. We additionally derive Barnes-Hut fast summation schemes for accelerated forward and adjoint dipole sum queries. These queries facilitate the use of ray tracing to efficiently and differentiably render images with our point-based representations, and thus update their point attributes to optimize scene geometry and appearance. We evaluate our method in inverse rendering applications against state-of-the-art alternatives, based on ray tracing of neural representations or rasterization of Gaussian point-based representations. Our method significantly improves 3D reconstruction quality and robustness at equal runtimes, while also supporting more general rendering methods such as shadow rays for direct illumination. 

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5. UnityPIC: Unity Point-Cloud Interactive Core

   https://doi.org/10.2312/pgv20211044  

   Wu, Y.; Vo, H.; Gong, J.; Zhu, Z. (June 2021, Parallel graphics and visualisation)

   M. Hadwiger, M. Larsen (Ed.)

   In this work, we present Unity Point-Cloud Interactive Core, a novel interactive point cloud rendering pipeline for the Unity Development Platform. The goal of the proposed pipeline is to expedite the development process for point cloud applications by encapsulating the rendering process as a standalone component, while maintaining flexibility through an implementable interface. The proposed pipeline allows for rendering arbitrarily large point clouds with improved performance and visual quality. First, a novel dynamic batching scheme is proposed to address the adaptive point sizing problem for level-of-detail (LOD) point cloud structures. Then, an approximate rendering algorithm is proposed to reduce overdraw by minimizing the overall number of fragment operations through an intermediate occlusion culling pass. For the purpose of analysis, the visual quality of renderings is quantified and measured by comparing against a high-quality baseline. In the experiments, the proposed pipeline maintains above 90 FPS for a 20 million point budget while achieving greater than 90% visual quality during interaction when rendering a point-cloud with more than 20 billion points. 

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