Point Primitives Research Articles

Fusion of Multiple Basic Element Features for Airborne LiDAR in-house Surveys

When using airborne LiDAR point clouds for city modelling and road extraction, point cloud classification is a crucial step. There are numerous ways for classifying point clouds, but there are still issues like redundant multi-dimensional feature vector data and poor point cloud classification in intricate situations. A point cloud classification method built on the fusing of multikernel feature vectors is suggested as a solution to these issues. The technique employs random forest to classify point cloud data by merging colour information, and it extracts feature vectors based on point primitives and object primitives, respectively. In this study, a densely populated area was chosen as the study area. Light airborne LIDAR mounted on a delta wing was used to collect point cloud data at a low altitude (170 m) over a dense cross-course. The point cloud data were then combined, corrected, and enhanced with texture data, and the houses were vectorized on the point cloud. The accuracy of the results was then assessed. With a median inaccuracy of 4.8 cm and a point cloud data collection rate of 83.3%, using airborne LIDAR to measure house corners can significantly lighten the labour associated with external house corner measurements.This test extracts the texture information of point cloud data through the efficient processing of high-density point cloud data, providing a reference for the application of texture information of airborne LIDAR data and a clear understanding of its accuracy.

Forward inclusion functions for ray-tracing implicit surfaces

Using Lipschitz bounds as linear inclusion functions, we show that both Lipschitz-based ray-tracing and bottom-up inclusion functions can be used together in the same framework. We propose asymmetrical forward inclusion functions that are exact at the query point and can better encode the function’s behavior on a given interval; therefore well suited for iterative processing. We show how to derive the linear and quadratic versions of these inclusion functions either by bounding the derivatives or building bottom-up inclusion functions and combining these two. We show our results on density fields defined from point primitives with compactly supported kernels and Gaussian kernels as well as Hermite radial basis functions. We demonstrate that our method provides noticeable improvement for grazing rays and transparent rendering.

Software Rasterization of 2 Billion Points in Real Time

The accelerated collection of detailed real-world 3D data in the form of ever-larger point clouds is sparking a demand for novel visualization techniques that are capable of rendering billions of point primitives in real-time. We propose a software rasterization pipeline for point clouds that is capable of rendering up to two billion points in real-time (60 FPS) on commodity hardware. Improvements over the state of the art are achieved by batching points, enabling a number of batch-level optimizations before rasterizing them within the same rendering pass. These optimizations include frustum culling, level-of-detail (LOD) rendering, and choosing the appropriate coordinate precision for a given batch of points directly within a compute workgroup. Adaptive coordinate precision, in conjunction with visibility buffers, reduces the required data for the majority of points to just four bytes, making our approach several times faster than the bandwidth-limited state of the art. Furthermore, support for LOD rendering makes our software rasterization approach suitable for rendering arbitrarily large point clouds, and to meet the elevated performance demands of virtual reality applications.

A Precisely One-Step Registration Methodology for Optical Imagery and LiDAR Data Using Virtual Point Primitives

The registration of optical imagery and 3D Light Detection and Ranging (LiDAR) point data continues to be a challenge for various applications in photogrammetry and remote sensing. In this paper, the framework employs a new registration primitive called virtual point (VP) that can be generated from the linear features within a LiDAR dataset including straight lines (SL) and curved lines (CL). By using an auxiliary parameter (λ), it is easy to take advantage of the accurate and fast calculation of the one-step registration transformation model. The transformation model parameters and λs can be calculated simultaneously by applying the least square method recursively. In urban areas, there are many buildings with different shapes. Therefore, the boundaries of buildings provide a large number of SL and CL features and selecting properly linear features and transforming into VPs can reduce the errors caused by the semi-discrete random characteristics of the LiDAR points. According to the result shown in the paper, the registration precision can reach the 1~2 pixels level of the optical images.

SEMI-AUTOMATIC APPROACH FOR OPTICAL AND LIDAR DATA INTEGRATION USING PHASE CONGRUENCY MODEL AT MULTIPLE RESOLUTIONS

Abstract. In light of the ongoing urban sprawl reported in recent studies, accurate urban mapping is essential for assessing current status and evolve new policies, to overcome various social, environmental, and economic consequence. Imagery and LiDAR data integration densifies remotely sensed data with radiometric and geometric characteristics, respectively, for a precise segregation of different urban features. This study integrated aerial and LiDAR images using point primitives, which were obtained from running the Phase Congruency model as an image filter to detect edges and corner. The main objective is to study the effect of applying the filter at different spatial resolutions on the registration accuracy and processing time. The detected edge/corner points that are mutual in both datasets, were identified as candidate points. The Shape Context Descriptor method paired-up candidate points as final points based on a minimum correlation of 95%. Affine, second and third order polynomials, in addition to the Direct Linear Transformation models were applied for the image registration process using the two sets of final points. The models were solved using Least Squares adjustments, and validated by a set of 55 checkpoints. It was observed that with the decrease in spatial resolution, on one hand, the registration accuracy did not significantly vary. However, the consistency of the model development and model validation accuracies were enhanced, especially with the third order polynomial model. On the other hand, the number of candidate points decreased; consequently, the processing time significantly declined. The 3D LiDAR points were visualised based on the Red, Green, and Blue radiometric values that were inherited from the aerial photo. The qualitative inspection was very satisfactory, especially when examining the scene’s tiny details. In spite of the interactivity in determining the candidate points, the proposed procedure overcomes the dissimilarity between datasets in terms of acquisition technique and time, and widens the tolerance of accepting points as candidates by including points that are not traditionally considered (i.e. road intersections).

Point Primitives Based Virtual Surgery System

In order to achieve a high degree of visual realism in surgery simulation, we present a virtual surgery system framework, which is based on point primitives for the virtual surgery scene rendering and the biomechanical calculation of the soft tissue. To embody the superiority of this framework, two virtual surgery systems based on point primitives we developed are exhibited in this paper. Six critical functional modules were selected as representative of basic and advanced virtual surgery skill. These modules were: 1) point-based texture mapping; 2) deformation simulation; 3) cutting simulation; 4) tearing simulation; 5) dynamic texture mapping; and 6) 3-D display. These modules were elaborated by including working principle, execution process, and the performance of the algorithm. The experimental results have shown that point primitives-based virtual surgery systems obtained higher performance in terms of computational efficiency and rendering effect than traditional meshes-based virtual surgery system.

A high-resolution model for soft tissue deformation based on point primitives

Background and objectives: In order to achieve a high degree of visual realism in surgery simulation, we propose a new model, which is based on point primitives and continuous elastic mechanics theory, for soft tissue deformation, tearing and/or cutting.Methods: The model can be described as a two-step local high-resolution strategy. First, appropriate volumetric data are sampled and assigned with proper physical properties. Second, sparsely sampled points in non-deformed regions and densely-sampled points in the deformed zone are selected and evaluated. By using a meshless deformation model based on point primitives for all volumetric data, the affine transform matrix of collision points can be computed. The new positions of neighboring points in the collided surface can be then calculated, and more details in the local deformed zone can be obtained for rendering. Technical details about the derivations of the proposed model as well as its implementation are given.Results: The visual effects and computation cost of the proposed model are evaluated and compared with conventional primitives-based methods. Experimental results show that the proposed model provides users (trainees) with improved visual feedback while the computational cost is at the same magnitude of other similar methods.Conclusions: The proposed method is especially suitable for the simulation of soft tissue deformation and tearing because no grid information needs to be maintained. It can simulate soft tissue deformation in a high degree of authenticity with real-time performance. It could be considered implemented in the development of a mixed reality application of neurosurgery simulators in the future.

Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

In this study, an efficient technique for immersive visualization of point cloud data is described and analyzed, and a manual extraction method is proposed. With the advancement of reconstruction technologies, laser scanned point cloud data has gained importance in robotics, 3D model reconstruction, and other computer fields. The point cloud data from the laser scanner has been widely used to reconstruct into a realistic 3D model. The 3D visualization of an unprocessed point cloud data from the laser scanner is often necessary to perform visual analysis, interactive selection, and editing operations in 3D. For the interactive visualization of point cloud data, OpenGL point primitives are used to accelerate for proficient rendering. The manual editing operations are performed using octree-based neighbors within a radius search algorithm. Finally, the implementation and performance comparison between an octree and kd-tree radius search method are evaluated using real-world example datasets.

Building Façade Recognition Using Oblique Aerial Images

This study proposes a method to recognize façades from large-scale urban scenes based on multi-level image features utilizing a recently developed oblique aerial photogrammetry technique. The method involves the use of multi-level image features, a bottom-up feature extraction procedure to produce regions of interest through monoscopic analysis, and then a coarse-to-fine feature matching strategy to characterise and match the regions in a stereoscopic model. Feature extraction from typical urban Manhattan scenes is based on line segments. Windows are re-organised based on the spatial constraints of line segments and the homogeneous structure of the spectrum. Façades as regions of interest are successfully constructed with a remarkable single edge and evidence from windows to get rid of occlusion. Feature matching is hierarchically performed beginning from distinctive facades and regularly distributed windows to the sub-pixel point primitives. The proposed strategy can effectively solve ambiguity and multi-solution problems in the complex urban scene matching process, particularly repetitive and poor-texture façades in oblique view.

A Deformable Generic 3D Model of Haptoral Anchor of Monogenean

In this paper, a digital 3D model which allows for visualisation in three dimensions and interactive manipulation is explored as a tool to help us understand the structural morphology and elucidate the functions of morphological structures of fragile microorganisms which defy live studies. We developed a deformable generic 3D model of haptoral anchor of dactylogyridean monogeneans that can subsequently be deformed into different desired anchor shapes by using direct manipulation deformation technique. We used point primitives to construct the rectangular building blocks to develop our deformable 3D model. Point primitives are manually marked on a 2D illustration of an anchor on a Cartesian graph paper and a set of Cartesian coordinates for each point primitive is manually extracted from the graph paper. A Python script is then written in Blender to construct 3D rectangular building blocks based on the Cartesian coordinates. The rectangular building blocks are stacked on top or by the side of each other following their respective Cartesian coordinates of point primitive. More point primitives are added at the sites in the 3D model where more structural variations are likely to occur, in order to generate complex anchor structures. We used Catmull-Clark subdivision surface modifier to smoothen the surface and edge of the generic 3D model to obtain a smoother and more natural 3D shape and antialiasing option to reduce the jagged edges of the 3D model. This deformable generic 3D model can be deformed into different desired 3D anchor shapes through direct manipulation deformation technique by aligning the vertices (pilot points) of the newly developed deformable generic 3D model onto the 2D illustrations of the desired shapes and moving the vertices until the desire 3D shapes are formed. In this generic 3D model all the vertices present are deployed for displacement during deformation.

Smart Nearest Neighbors

This document presents an implementation of two algorithms, Voronoi Neighbors and Binary Space Partition (BSP) Neighbors. These algorithms find neighbors of a point in a point set that are somehow better'' than aK nearest neighbors’’ or a ``all neighbors within a radius’’ query. This type of nearest neighbor query is more computationally expensive, but results in set of neighbors with more desirable properties. The BSP Neighbors search ensures that there is less local duplication, while the Voronoi Neighbors search ensures that the spatial arrangement of the neighbors is as uniform as possible.These algorithms are explained in ``Point Primitives for Interactive Modeling and Processing of 3D Geometry’’.The code is available here: https://github.com/daviddoria/SmartNearestNeighbors

A hardware architecture for surface splatting

We present a novel architecture for hardware-accelerated rendering of point primitives. Our pipeline implements a refined version of EWA splatting, a high quality method for antialiased rendering of point sampled representations. A central feature of our design is the seamless integration of the architecture into conventional, OpenGL-like graphics pipelines so as to complement triangle-based rendering. The specific properties of the EWA algorithm required a variety of novel design concepts including a ternary depth test and using an on-chip pipelined heap data structure for making the memory accesses of splat primitives more coherent. In addition, we developed a computationally stable evaluation scheme for perspectively corrected splats. We implemented our architecture both on reconfigurable FPGA boards and as an ASIC prototype, and we integrated it into an OpenGL-like software implementation. Our evaluation comprises a detailed performance analysis using scenes of varying complexity.

A hardware architecture for surface splatting

We present a novel architecture for hardware-accelerated rendering of point primitives. Our pipeline implements a refined version of EWA splatting, a high quality method for antialiased rendering of point sampled representations. A central feature of our design is the seamless integration of the architecture into conventional, OpenGL-like graphics pipelines so as to complement triangle-based rendering. The specific properties of the EWA algorithm required a variety of novel design concepts including a ternary depth test and using an on-chip pipelined heap data structure for making the memory accesses of splat primitives more coherent. In addition, we developed a computationally stable evaluation scheme for perspectively corrected splats. We implemented our architecture both on reconfigurable FPGA boards and as an ASIC prototype, and we integrated it into an OpenGL-like software implementation. Our evaluation comprises a detailed performance analysis using scenes of varying complexity.

Extreme simplification and rendering of point sets using algebraic multigrid

We present a novel approach for extreme simplification of point set models, in the context of real-time rendering. Point sets are often rendered using simple point primitives, such as oriented discs. However, this requires using many primitives to render even moderately simple shapes. Often, one wishes to render a simplified model using only a few primitives, thus trading accuracy for simplicity. For this goal, we propose a more complex primitive, called a splat, that is able to approximate larger and more complex surface areas than oriented discs. We construct our primitive by decomposing the model into quasi-flat regions, using an efficient algebraic multigrid algorithm. Next, we encode these regions into splats implemented as planar support polygons textured with color and transparency information and render the splats using a special blending algorithm. Our approach combines the advantages of mesh-less point-based techniques with traditional polygon-based techniques. We demonstrate our method on various models.

Getting to the Point...?

This article explains the usefulness and versatility of point primitives for graphics, visualization, rendering, modeling, and animation. The observations and findings are based on more than 8 years of research in point-based graphics and on many discussions with key researchers in this rapidly developing field. This article is intended to provide both a critical analysis and an inspiration for researchers and developers

Compression and rendering of iso-surfaces and point sampled geometry

In this paper we present a streaming compression scheme for gigantic point sets including per-point normals. This scheme extends on our previous Duodecim approach [21] in two different ways. First, we show how to use this approach for the compression and rendering of high-resolution iso-surfaces in volumetric data sets. Second, we use deferred shading of point primitives to considerably improve rendering quality. Iso-surface reconstruction is performed in a hexagonal close packing (HCP) grid, into which the initial data set is resampled. Normals are resampled from the initial domain using volumetric gradients. By incremental encoding, only slightly more than 3 bits per surface point and 5 bits per surface normal are required at high fidelity. The compressed data stream can be decoded in the graphics processing unit (GPU). Decoded point positions are saved in graphics memory, and they are then used on the GPU again to render point primitives. In this way high quality gigantic data sets can directly be rendered from their compressed representation in local GPU memory at interactive frame rates (see Fig. 1).

Modeling and rendering of points with local geometry

We present a novel rendering primitive that combines the modeling brevity of points with the rasterization efficiency of polygons. The surface is represented by a sampled collection of Differential Points (DP), each with embedded curvature information that captures the local differential geometry in the vicinity of that point. This is a more general point representation that, for the cost of a few additional bytes, packs much more information per point than the traditional point-based models. This information is used to efficiently render the surface as a collection of local geometries. To use the hardware acceleration, the DPs are quantized into 256 different types and each sampled point is approximated by the closest quantized DP and is rendered as a normal-mapped rectangle. The advantages to this representation are: 1) The surface can be represented more sparsely compared to other point primitives, 2) it achieves a robust hardware accelerated per-pixel shading - even with no connectivity information, and 3) it offers a novel point-based simplification technique that factors in the complexity of the local geometry. The number of primitives being equal, DPs produce a much better quality of rendering than a pure splat-based approach. Visual appearances being similar, DPs are about two times faster and require about 75 percent less disk space in comparison to splatting primitives.