AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-time High-Fidelity LiDAR Simulation

LiDAR sensors provide rich 3D information about their surrounding and are becoming increasingly important for autonomous vehicles tasks, such as semantic segmentation, object detection, and tracking. Simulating a LiDAR sensor accelerates the testing, validation, and deployment of autonomous vehicles, while reducing the cost and eliminating the risks of testing in real-world scenarios. We address the problem of high-fidelity LiDAR simulation and present a pipeline that leverages real-world point clouds acquired by mobile mapping systems. Point-based geometry representations, more specifically splats, have proven their ability to accurately model the underlying surface in very large point clouds. We introduce an adaptive splats generation method that accurately models the underlying 3D geometry, especially for thin structures. Moreover, we introduce a physics-based, faster-than-real-time LiDAR simulator, in the splatted model, leveraging the GPU parallel architecture with an acceleration structure, while focusing on efficiently handling large point clouds. We test our LiDAR simulation in real-world conditions, showing qualitative and quantitative results compared to basic splatting and meshing techniques, demonstrating the interest of our modeling technique.Comment: 28 pages, 11 figures, 6 table

Ray Tracing Methods for Point Cloud Rendering

State of the art scanning and capturing devices are able to produce surface point cloud models of a wide range of real world objects. The visualization and rendering of enormous point clouds with millions or billions of points is demanding. VR- and AR-applications can utilize embedded real world objects in generating visually pleasing and immersive virtual worlds. In order to achieve convincing real life equivalents in VR, rendering techniques that can replicate realistic material and lighting effects are needed. This can be achieved by utilizing ray tracing methods to render the virtual world onto a monitor or a head-mounted display. Virtual reality applications need real-time stereoscopic rendering with high frame rates and resolution to produce a realistic and comfortable experience. This sets high demands on a point cloud ray tracing pipeline, which needs efficient intersection testing between rays and point cloud models. An easily intersectable global surface can be reconstructed from the point cloud model with, e.g., triangle mesh reconstruction. However, this can be computationally demanding and even wasteful if parts of the model are out of view or occluded. Direct point cloud ray tracing methods consider local features of the point cloud to generate intersectable surfaces only when needed. In this thesis, we survey and compare different methods for directly ray tracing point cloud models without global surface reconstruction. Methods are compared with asymptotic complexity analysis and it is concluded that direct ray tracing of point clouds can be computationally more efficient compared to global surface reconstruction

Multiresolution Ray Tracing For Point-Based Geometry [QA445. N832 2007 f rb].

Tumpuan utama di dalam tesis ini adalah kajian tentang integrasi teknik berbilang peleraian dengan penyurihan sinar di dalam menjanakan imej objek objek 3D berasas titik. The primary concern in this thesis is with the incorporation of multiresolutionbased optimization into ray tracing algorithms specially tailored for point-based geometry

Cone carving for surface reconstruction

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Meshless Mechanics and Point-Based Visualization Methods for Surgical Simulations

Computer-based modeling and simulation practices have become an integral part of the medical education field. For surgical simulation applications, realistic constitutive modeling of soft tissue is considered to be one of the most challenging aspects of the problem, because biomechanical soft-tissue models need to reflect the correct elastic response, have to be efficient in order to run at interactive simulation rates, and be able to support operations such as cuts and sutures. Mesh-based solutions, where the connections between the individual degrees of freedom (DoF) are defined explicitly, have been the traditional choice to approach these problems. However, when the problem under investigation contains a discontinuity that disrupts the connectivity between the DoFs, the underlying mesh structure has to be reconfigured in order to handle the newly introduced discontinuity correctly. This reconfiguration for mesh-based techniques is typically called dynamic remeshing, and most of the time it causes the performance bottleneck in the simulation. In this dissertation, the efficiency of point-based meshless methods is investigated for both constitutive modeling of elastic soft tissues and visualization of simulation objects, where arbitrary discontinuities/cuts are applied to the objects in the context of surgical simulation. The point-based deformable object modeling problem is examined in three functional aspects: modeling continuous elastic deformations with, handling discontinuities in, and visualizing a point-based object. Algorithmic and implementation details of the presented techniques are discussed in the dissertation. The presented point-based techniques are implemented as separate components and integrated into the open-source software framework SOFA. The presented meshless continuum mechanics model of elastic tissue were verified by comparing it to the Hertzian non-adhesive frictionless contact theory. Virtual experiments were setup with a point-based deformable block and a rigid indenter, and force-displacement curves obtained from the virtual experiments were compared to the theoretical solutions. The meshless mechanics model of soft tissue and the integrated novel discontinuity treatment technique discussed in this dissertation allows handling cuts of arbitrary shape. The implemented enrichment technique not only modifies the internal mechanics of the soft tissue model, but also updates the point-based visual representation in an efficient way preventing the use of costly dynamic remeshing operations

Point Cloud Clustering Using Panoramic Layered Range Image

Point-cloud clustering is an essential technique for modeling massive point clouds acquired with a laser scanner. There are three clustering approaches in point-cloud clustering, namely model-based clustering, edge-based clustering, and region-based clustering. In geoinformatics, edge-based and region-based clustering are often applied for the modeling of buildings and roads. These approaches use low-resolution point-cloud data that consist of tens of points or several hundred points per m2, such as aerial laser scanning data and vehicle-borne mobile mapping system data. These approaches also focus on geometrical knowledge and restrictions. We focused on region-based point-cloud clustering to improve 3D visualization and modeling using massive point clouds. We proposed a point-cloud clustering methodology and point-cloud filtering on a multilayered panoramic range image. A point-based rendering approach was applied for the range image generation using a massive point cloud. Moreover, we conducted three experiments to verify our methodology

Multiresolution Ray Tracing For Point-Based Geometry

Tumpuan utama di dalam tesis ini adalah kajian tentang integrasi teknik berbilang peleraian dengan penyurihan sinar di dalam menjanakan imej objekobjek 3D berasas titik. The primary concern in this thesis is with the incorporation of multiresolutionbased optimization into ray tracing algorithms specially tailored for point-based geometry