In recent years, the application and research of three-dimensional laser scanning technology in tunnel engineering has continuously emerged, while the shortcomings still exists. This paper conducts research on quick preprocessing method of tunnel point cloud data based on three-dimensional laser scanning. For streamlining the tunnel point cloud model data, comparison analysis discusses are applied to both the downsampling algorithm and the point cloud denoising method respectively. Simulation experiments are introduced to compare the effects of different downsampling algorithms applied to the tunnel point cloud model, analyze the sampling efficiency and performance of each algorithm. Combining with statistical denoising and radius denoising algorithms, a distance denoising based on an iterative filtering model method is proposed. The result shows that this method is suitable for the tunnel point cloud model during the construction period with complex environment, and can effectively eliminate most of the point cloud noise.

https://doi.org/10.1088/1742-6596/2185/1/012038

Research on Fast Pre-Processing Method of Tunnel Point Cloud Data in Complex Environment

Video Up-scaling is a hotspot in TV display area; as an important brunch of Video Up-scaling, Texture-Based Video Upscaling (TBVU) method shows great potential of hardware implementation. Coarse-grained Reconfigurable Architecture (CGRA) is a very promising processor; it is a parallel computing platform which provides high performance of hardware, high flexibility of software, and dynamical reconfiguration ability. In this paper we propose an implementation of TBVU on CGRA. We fully exploit the characters of TBVU and utilize several techniques to reduce memory I/O operation and total execution time. Experimental results show that our work can greatly reduce the I/O operation and the execution time compared with the non-optimized ones. We also compare our work with other platforms and find great advantage in execution time and resource utilization rate. key words: reconfigurable architecture, video up-scaling, texture, parallelization, interpolation, sobel method, bilinear, bicubic

The Implementation of Texture-Based Video Up-Scaling on Coarse-Grained Reconfigurable Architecture

We present a multi-level partition of unity algebraic set surfaces (MPU-APSS) for surface reconstruction which can be represented by either a projection or in an implicit form. An algebraic point set surface (APSS) defines a smooth surface from a set of unorganized points using local moving least-squares (MLS) fitting of algebraic spheres. However, due to the local nature, APSS does not work well for geometry editing and modeling. Instead, our method builds an implicit approximation function for the scattered point set based on the partition of unity approach. By using an octree subdivision strategy, we first adaptively construct local algebraic spheres for the point set, and then apply weighting functions to blend together these local shape functions. Finally, we compute an error-controlled approximation of the signed distance function from the surface. In addition, we present an efficient projection operator which makes our representation suitable for point set filtering and dynamic point resampling. We demonstrate the effectiveness of our unified approach for both surface reconstruction and geometry modeling such as surface completion.

Multi-level partition of unity algebraic point set surfaces

We present a simple, fast, and smooth scheme to approximate Algebraic Point Set Surfaces using non-compact kernels, which is particularly suited for filtering and reconstructing point sets presenting large missing parts. Our key idea is to consider a moving level-of-detail of the input point set which is adaptive w.r.t. to the evaluation location, just such as the samples weights are output sensitive in the traditional moving least squares scheme. We also introduce an adaptive progressive octree refinement scheme, driven by the resulting implicit surface, to properly capture the modeled geometry even far away from the input samples. Similarly to typical compactly-supported approximations, our operator runs in logarithmic time while defining high quality surfaces even on challenging inputs for which only global optimizations achieve reasonable results. We demonstrate our technique on a variety of point sets featuring geometric noise as well as large holes.

Moving level-of-detail surfaces

Pavement roughness serves as a crucial indicator for evaluating road performance. However, traditional measurement methods, such as laser detection vehicles, are limited to providing roughness values for a single profile, failing to capture the overall pavement condition comprehensively. To address this limitation, this study utilized high-precision light detection and ranging technology (LiDAR) to acquire three-dimensional point cloud data for a 25 km road section in Shanghai. Road elevations were extracted from different lateral survey lines. Subsequently, variance analysis and the Kruskal–Wallis non-parametric test were conducted to evaluate the differences in the lateral distribution and longitudinal variability of the pavement roughness. The findings revealed significant differences in the international roughness index (IRI) among the survey lines within the road section. Moreover, the observed variations in the lateral distribution of pavement roughness were influenced by the characteristics of the road section itself. Roads exhibiting discrete roughness patterns displayed a higher likelihood of significant detection disparities. Additionally, it was discovered that the discrepancy between the detection length and the actual road length introduced volatility in repeated detection results, necessitating a limitation of this discrepancy to 30 m. Consequently, it has been recommended to consider the lateral distribution of pavement roughness and to regulate the detection length in road performance evaluations to enhance reliability and facilitate more accurate maintenance decision making. The study highlights the importance of incorporating comprehensive assessment approaches for pavement roughness in road management practices.

Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data

We present a multi-level partition of unity algebraic set surfaces (MPU-APSS) for surface reconstruction which can be represented by either a projection or in an implicit form. A algebraic point set surface (APSS) defines a smooth surface from a set of unorganized points using local moving least-squares (MLS) fitting of algebraic spheres. However, due to the local nature, APSS does not work well for geometry editing and modeling. Instead, our method builds a global implicit approximation function for the scattered pointset based on the partition of unity approach. By using an octree subdivision strategy, we first adaptively construct local algebraic spheres for the point set, and then apply weighting functions to blend together these local shape functions. Finally, we compute an error-controlled approximation of the signed distance function from the surface. In addition, we present an efficient projection operator which makes our representation suitable for point set filtering and dynamic point resampling. We demonstrate the effectiveness of our unified approach for both surface reconstruction and geometry modeling such as shape blending, surface completion and deformations.

/pdf/multi-level-partition-of-unity-algebraic-point-set-surfaces-2abao03537.pdf

Multi-level Partition of Unity Algebraic Point Set Surfaces

We present a novel approach for upsampling the synthesized image and video texture using a multi-scale joint bilateral filter. Our method is based on the motivation: if the available exemplar texture is used as a prior to upsample the synthesized texture, a high resolution result that better preventing image blurring can be obtained. Our joint bilateral upsampling applies a spatial filter on the synthesized texture, and jointly applies a similar range filter on exemplar texture which guides the interpolation from low to high resolution. To further enhance the detail of the upsampled texture, we propose multi-scale joint bilateral upsampling method which progressively enhances the detail of the upsampled texture. In addition, we present a detail-aware texture optimization approach which combines texture optimization and histogram matching of the image detail to improve the quality of the synthesized results. Finally, we present an accelerated joint bilateral filter, which enables our upsampling process to interactively generate a large texture. We show results for upsampling image and video texture and compare them to traditional upsampling methods, which illustrate that our methods require low computational and memory costs while receive better results.

Multie-scale joint bilateral image and video texture upsampling

We present a fast gradient domain video compositing using hierarchical data structure which subdivides the compositing region into an octree data. To handle large video compositing by solving a 3D Poisson equation, the derived linear system is usually large. To solve the system requires large memory space and long computational time, which makes it intractable on a standard computer. To address the scalability problem, rather than compositing the video in the gradient-domain pixel by pixel, we perform the video compositing in a reduced space using octree data structure, which significantly reduces the variables. We show that the proposed octree approach is efficient in both seamless and mixing gradient-domain video compositing. Our method enables to perform video compositing in greatly reduced computational time and memory space, while receiving visually identical results with that computed from the full solution.

Guangpu Feng

Papers

Multi-level Partition of Unity Algebraic Point Set Surfaces

Multie-scale joint bilateral image and video texture upsampling

Fast gradient-domain video compositing using hierarchical data structure