Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such ...visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset.
The availability of high resolution and long range laser range finder with colour image registration facilities opens up the possibility of large scale, accurate and dense 3D environment modelling. ...This paper addresses the problem of integration and analysis of multiple scans collected over extended regions for stereoscopic walk-through visualisation. Video clip examples will be shown at the time of paper presentation.
The Rockery is often a key element of a Classical Chinese Garden. It’s exquisite detailed physical characteristics a major contributor to artistic value, aesthetic appeal, and the carrier of ...historical and cultural heritage values. Poets and scholars have often described the beauty of these places in classical gardens in qualitative terms but lacked the quantitative tools to provide replicable metric descriptions. The highly complex forms and surfaces, irregularity, and fragility of garden rockeries has challenged authors to accurately describe the characteristics of these qualities using traditional methods and tools. This article presents a new method of digital characterization approach based on laser scanning and point cloud visualization, which can quantitatively detect and represent the pattern of rockery surface textures. It offers a replicable accurate quantitative descriptor of the Classical Chinese rockery. The Small-Rock Mountain Retreat, a nationally protected rockery garden in China, has been used as a case study. It contains original historic elements and more recently restored areas. Two characteristics of rockery surfaces, including the well-proportioned density and space, and the proper contrast between solid and void, were analyzed by examining four attributes: (1) surface complexity; (2) contour curvature; (3) shape variation; and (4) the interweaving of lightness and darkness. The findings demonstrate that, despite some similarities between the restored portion of the rockery and the historical remnants, there are variances in the richness of the details and the balanced distribution of shape change. The digital characterization approach introduced in this article offers a new perspective for recording and in turn safeguarding Chinese garden rockeries and other irregular cultural heritage objects.
Current state-of-the-art point cloud visualization techniques have shortcomings when dealing with sparse and less accurate data or close-up interactions. In this paper, we present a visualization ...technique called stroke-based splatting, which applies concepts of stroke-based rendering to surface-aligned splatting, allowing for better shape perception at lower resolutions and close-ups. We create a painterly depiction of the data with an impressionistic aesthetic, which is a metaphor the user is culturally trained to recognize, thus attributing higher quality to the visualization. This is achieved by shaping each object-aligned splat as a brush stroke, and orienting it according to globally coherent tangent vectors from the Householder formula, creating a painterly depiction of the scanned cloud. Each splat is sized according to a color-based clustering analysis of the data, ensuring the consistency of brush strokes within neighborhood areas. By controlling brush shape generation parameters and blending factors between neighboring splats, the user is able to simulate different painting styles in real time. We have tested our method with data sets captured by commodity laser scanners as well as publicly available high-resolution point clouds, both having highly interactive frame rates in all cases. In addition, a user study was conducted comparing our approach to state-of-the-art point cloud visualization techniques. Users considered stroke-based splatting a valuable technique as it provides a higher or similar visual quality to current approaches.
This paper aims to use the improved Generative Adversarial Network (GAN) model for Three Dimensional (3D) animation graphic design, improve the efficiency of 3D animation graphic design, and promote ...the accuracy of model recognition. It acquires 3D animated scene color images from different perspectives. This paper performs 3D visualization through point clouds, outputs high-quality point cloud results, and uses Convolutional Neural Network (CNN), Earth-Mover (EM) distance, and Least Squares Method (LSM) to improve the GAN model. Finally, the effectiveness of the improved GAN in the graphic design of 3D animation scenes and the effects of different improved models in generating 3D animation scene images are analyzed. The results show that the computational loss amplitude of the improved GAN model using Label Smoothing processing deep convolutional neural network is between 2, 3. The generator loss variation is smaller, and the image quality of the generated 3D animation scene is gradually improved. The training process of the LSM-improved model is more stable, and the loss value is lower than that of the EM distance improved model. The loss value of the generator is 0.3,0.5, and the loss value of the discriminator is 0.1,0.2. The Inception score of the LSM-improved model is 0.0297 higher than that of the CNN-improved model and the EM distance improved model and 0.0198 higher than that of the GAN model.
The analysis of high-dimensional data is an important, yet inherently difficult problem. Projection techniques such as Principal Component Analysis, Multi-dimensional Scaling and Self-Organizing Map ...can be used to map high-dimensional data to 2D display space. However, projections typically incur a loss in information. Often, uncertainty exists regarding the precision of the projection as compared with its original data characteristics. While the output quality of these projection techniques can be discussed in terms of aggregate numeric error values, visualization is often helpful for better understanding the projection results. We address the visual assessment of projection precision by an approach integrating an appropriately designed projection precision measure directly into the projection visualization. To this end, a flexible projection precision measure is defined that allows the user to balance the degree of locality at which the measure is evaluated. Several visual mappings are designed for integrating the precision measure into the projection visualization at various levels of abstraction. The techniques are implemented in an interactive system, including methods supporting the user in finding appropriate settings of relevant parameters. We demonstrate the usefulness of the approach for visual analysis of classified and unclassified high-dimensional data sets. We show how our interactive precision quality visualization system helps to examine the preservation of original data properties in projected space.
We propose a novel unsupervised machine learning approach for effective semantic labeling by combining two different multi-class classifications, structural and contextual classification, of points ...in airborne LiDAR point cloud of urban environment. Structural classification labels a point in the cloud as a point-, line-, or surface-type feature. An additional outcome of this classification is the geometry-preserving downsampling of the point cloud. The contextual classification, on the other hand, labels the points in four classes, namely, buildings, vegetation, natural ground, and asphalt ground, by using data derived from the raw input, which includes the structural classification. Preserving these two classifications in the labeling of the points gives a geometry-aware contextual semantic labeling. We propose: (a) an augmented semantic classification which preserves both structural and contextual classification, (b) an interactive hierarchical clustering method for contextual classification, and (c) an interactive visual analytic framework to aid both the structural and contextual classifications.
We develop an efficient point cloud visualization framework. For efficient navigation in the visualization, we introduce a spline-based technique for the smooth approximation of discrete distance ...field data. Implemented on the GPU, the approximation technique allows for efficient visualizations and smooth zooming in and out of the distance field data. Combined with a template set of predefined, automatically or interactively adjustable transfer functions, the smooth distance field representation allows for an effective visualization of point cloud data at random abstraction levels. Using the presented technique, sets of point clouds can be effectively analyzed for intra- and inter-point cloud distribution characteristics. The effectiveness and usefulness of our approach is demonstrated by application on various point cloud visualization problems.
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