Image-based non-photorealistic rendering for realtime virtual sculpting

Lü, Ping; Sheng, Bin; Luo, Shiping; Jia, Xia; Wu, Wen

doi:10.1007/s11042-014-2146-4

Cited by 15 publications

(6 citation statements)

References 32 publications

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“…He Xing Wu [26] realized the automatic gain compensation algorithm through the GPU parallel mean filtering process, and the speed was increased by about 267 times. Ping Lu et al [27] implemented an inverse Gaussian bilateral filter on GPU, which was applied to a nonphotorealistic real-time virtual engraving system. Herng-Hua Chang et al [28] proposed a bilateral filter based on CUDA and established a "high precision" parameter prediction system, which improved the processing speed.…”

Section: Related Research Workmentioning

confidence: 99%

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

et al. 2021

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The image will be contaminated by noise during the imaging process, which severely degrades the image quality. It is necessary to filter the collected image. With the increasing amount of image data, the traditional single-processor or multiprocessor computing equipment has been unable to meet the requirements of real-time data processing. In this paper, the computational model of weighted mean filtering and the characteristics of high performance computer architecture are studied. An efficient hierarchical image weighted mean filtering parallel algorithm for Open Computing Language (OpenCL) is designed and implemented, which can fully express the parallelism of the computing model. The parallel algorithm takes full account of the characteristics of image discrete convolution computing and the multilayer logic architecture of high performance computer, deeply excavates the parallelism of the computing platform and computing model, and realizes the efficient task mapping from computing model to computing resources. The model is implemented in parallel with the two levels of work-group and workitem. The experimental results show that compared with the serial algorithm based on CPU, the parallel algorithm based on Open Multi-Processing (OpenMP) and the parallel algorithm based on Compute Unified Device Architecture (CUDA), the parallel algorithm of weighted mean filtering achieves 20.88 times, 18.52 times and 1.26 times acceleration ratio on the NVIDIA GPU computing platform based on OpenCL architecture, respectively. It realizes better computing performance and runs on different Graphic Processing Unit (GPU) computing platforms, and has good portability and scalability.INDEX TERMS weighted mean filtering; Gaussian noise; Graphic Processing Unit (GPU); Open Computing Language (OpenCL); parallel algorithm.

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Section: Related Research Workmentioning

confidence: 99%

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

et al. 2021

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“…A head mounted display and a head mounted RGBD camera is used to track the hands and head of the user in the 3D space as well as to render the virtual sculpted object seamlessly blended with the real world scene. Lu et al [LSL∗15] attempt to provide an interface for a non‐photorealistic rendering of a scene augmented with the virtual content created using sculpting in real‐time. The surfaces are deformed using haptic interaction and the boundaries for each element of the scene is highlighted using the techniques for edge detection.…”

Section: Virtual Sculpting ‐ Another Paradigm For Content Creationmentioning

confidence: 99%

A Survey on Sketch Based Content Creation: from the Desktop to Virtual and Augmented Reality

Bhattacharjee

Chaudhuri

2020

Computer Graphics Forum

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Sketching is one of the most natural ways for representing any object pictorially. It is however, challenging to convert sketches to 3D content that is suitable for various applications like movies, games and computer aided design. With the advent of more accessible Virtual Reality (VR) and Augmented Reality (AR) technologies, sketching can potentially become a more powerful yet easy‐to‐use modality for content creation. In this state‐of‐the‐art report, we aim to present a comprehensive overview of techniques related to sketch based content creation, both on the desktop and in VR/AR. We discuss various basic concepts related to static and dynamic content creation using sketches. We provide a structured review of various aspects of content creation including model generation, coloring and texturing, and finally animation. We try to motivate the advantages that VR/AR based sketching techniques and systems can offer into making sketch based content creation a more accessible and powerful mode of expression. We also discuss and highlight various unsolved challenges that current sketch based techniques face with the goal of encouraging future research in the domain.

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“…As a result, it is 5.52 times faster than the original version. In [37], Lu et al discussed a nonphotorealistic real-time virtual sculpting system. Via touchenabled manipulation, haptic interaction is conducted to form the deformable surfaces and the edge extraction is employed to stress boundaries.…”

Section: Related Workmentioning

confidence: 99%

Parallel Fast Pencil Drawing Generation Algorithm Based on GPU

Qiu

Liu

et al. 2019

IEEE Access

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With the development of image processing technology, pencil drawing has been widely used in video games and mobile phone applications. However, the existing pencil drawing algorithms require a large amount of time to convert a real picture into a pencil drawing; hence, it is difficult to apply them to real-time systems. This paper proposes a parallel fast pencil drawing generation algorithm based on the graphics processing unit (GPU) to accelerate the real-time rendering process of sketch painting. The parallelism of the pencil drawing generation algorithm is identified via a theoretical analysis at first. Then, sub-algorithms of the sequential algorithm are designed in parallel using the compute unified device architecture (CUDA) programming model and executed via thread-level parallel techniques. Furthermore, an optimal cache pattern of data that reduce the access time of the most frequently used data is structured using shared memory and constant memory. Finally, task-level parallelism is achieved by the CUDA stream technology, which overlaps independent sub-tasks for further acceleration. On the CUDA platform, the experimental results demonstrate that the proposed parallel algorithm can achieve a significant increase in speedup. The proposed algorithm achieves a performance improvement of 448.59 times compared with the sequential algorithm, on 2560×1920-resolution images, and maintains a high degree of similarity with the real pencil paintings. Hence, the proposed algorithm is suitable for real-time pencil drawing rendering and has promising application prospects in non-photorealistic rendering. INDEX TERMS Non-photorealistic rendering, pencil drawing, parallel algorithm, GPU platform, convolution operation, CUDA.

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Image-based non-photorealistic rendering for realtime virtual sculpting

Cited by 15 publications

References 32 publications

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

A Survey on Sketch Based Content Creation: from the Desktop to Virtual and Augmented Reality

Parallel Fast Pencil Drawing Generation Algorithm Based on GPU

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