Computer Graphics World最新文献_第3页

GPU accelerated scalable parallel coordinates plots GPU加速可伸缩的平行坐标图

Computer Graphics World Pub Date : 2022-11-01 DOI: 10.2139/ssrn.4188415

Josef Stumpfegger, Kevin Höhlein, George E. Craig, R. Westermann

引用次数: 1

Developable mesh segmentation by detecting curve-like features on Gauss images 基于高斯图像曲线特征的可展开网格分割

Computer Graphics World Pub Date : 2022-10-01 DOI: 10.2139/ssrn.4126876

Zheng Zeng, Xiaohong Jia, L. Shen, Pengbo Bo

引用次数: 0

Structure simplification of planar quadrilateral meshes 平面四边形网格的结构简化

Computer Graphics World Pub Date : 2022-10-01 DOI: 10.2139/ssrn.4167992

Muhammad Naeem Akram, Kaoji Xu, Guoning Chen

引用次数: 1

Understanding reinforcement learned crowds 理解强化学习群体

Computer Graphics World Pub Date : 2022-09-19 DOI: 10.48550/arXiv.2209.09344

Ariel Kwiatkowski, Vicky S. Kalogeiton, Julien Pettr'e, Marie-Paule Cani

{"title":"Understanding reinforcement learned crowds","authors":"Ariel Kwiatkowski, Vicky S. Kalogeiton, Julien Pettr'e, Marie-Paule Cani","doi":"10.48550/arXiv.2209.09344","DOIUrl":"https://doi.org/10.48550/arXiv.2209.09344","url":null,"abstract":"Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual agents, however they often make different design choices when it comes to the fundamental simulation setup. Each of these choices comes with a reasonable justification for its use, so it is not obvious what is their real impact, and how they affect the results. In this work, we analyze some of these arbitrary choices in terms of their impact on the learning performance, as well as the quality of the resulting simulation measured in terms of the energy efficiency. We perform a theoretical analysis of the properties of the reward function design, and empirically evaluate the impact of using certain observation and action spaces on a variety of scenarios, with the reward function and energy usage as metrics. We show that directly using the neighboring agents' information as observation generally outperforms the more widely used raycasting. Similarly, using nonholonomic controls with egocentric observations tends to produce more efficient behaviors than holonomic controls with absolute observations. Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"37 1","pages":"28-37"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90233882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions 基于自学习策略和高强度区域注意机制的水下增强

Computer Graphics World Pub Date : 2022-08-01 DOI: 10.48550/arXiv.2208.03319

Claudio D. Mello, Bryan U. Moreira, Paulo J. O. Evald, Paulo L. J. Drews-Jr, Silvia S. Botelho

{"title":"Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions","authors":"Claudio D. Mello, Bryan U. Moreira, Paulo J. O. Evald, Paulo L. J. Drews-Jr, Silvia S. Botelho","doi":"10.48550/arXiv.2208.03319","DOIUrl":"https://doi.org/10.48550/arXiv.2208.03319","url":null,"abstract":"Images acquired during underwater activities suffer from environmental properties of the water, such as turbidity and light attenuation. These phenomena cause color distortion, blurring, and contrast reduction. In addition, irregular ambient light distribution causes color channel unbalance and regions with high-intensity pixels. Recent works related to underwater image enhancement, and based on deep learning approaches, tackle the lack of paired datasets generating synthetic ground-truth. In this paper, we present a self-supervised learning methodology for underwater image enhancement based on deep learning that requires no paired datasets. The proposed method estimates the degradation present in underwater images. Besides, an autoencoder reconstructs this image, and its output image is degraded using the estimated degradation information. Therefore, the strategy replaces the output image with the degraded version in the loss function during the training phase. This procedure textit{misleads} the neural network that learns to compensate the additional degradation. As a result, the reconstructed image is an enhanced version of the input image. Also, the algorithm presents an attention module to reduce high-intensity areas generated in enhanced images by color channel unbalances and outlier regions. Furthermore, the proposed methodology requires no ground-truth. Besides, only real underwater images were used to train the neural network, and the results indicate the effectiveness of the method in terms of color preservation, color cast reduction, and contrast improvement.","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"56 1","pages":"264-276"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83928855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild 主题:基于草图的野外三维形状检索

Computer Graphics World Pub Date : 2022-07-01 DOI: 10.48550/arXiv.2207.04945

Jie Qin, Shuaihang Yuan, Jiaxin Chen, B. Amor, Yi Fang, N. Hoang-Xuan, Chi-Bien Chu, Khoi-Nguyen Nguyen-Ngoc, Thien-Tri Cao, Nhat-Khang Ngô, Tuan-Luc Huynh, Hai-Dang Nguyen, M. Tran, H. Luo, Jianning Wang, Zheng-Wei Zhang, Zihao Xin, Yang Wang, Feng Wang, Yingjie Tang, Haiqin Chen, Yan Wang, Qunying Zhou, Ji Zhang, Hongyu Wang

{"title":"SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild","authors":"Jie Qin, Shuaihang Yuan, Jiaxin Chen, B. Amor, Yi Fang, N. Hoang-Xuan, Chi-Bien Chu, Khoi-Nguyen Nguyen-Ngoc, Thien-Tri Cao, Nhat-Khang Ngô, Tuan-Luc Huynh, Hai-Dang Nguyen, M. Tran, H. Luo, Jianning Wang, Zheng-Wei Zhang, Zihao Xin, Yang Wang, Feng Wang, Yingjie Tang, Haiqin Chen, Yan Wang, Qunying Zhou, Ji Zhang, Hongyu Wang","doi":"10.48550/arXiv.2207.04945","DOIUrl":"https://doi.org/10.48550/arXiv.2207.04945","url":null,"abstract":"Sketch-based 3D shape retrieval (SBSR) is an important yet challenging task, which has drawn more and more attention in recent years. Existing approaches address the problem in a restricted setting, without appropriately simulating real application scenarios. To mimic the realistic setting, in this track, we adopt large-scale sketches drawn by amateurs of different levels of drawing skills, as well as a variety of 3D shapes including not only CAD models but also models scanned from real objects. We define two SBSR tasks and construct two benchmarks consisting of more than 46,000 CAD models, 1,700 realistic models, and 145,000 sketches in total. Four teams participated in this track and submitted 15 runs for the two tasks, evaluated by 7 commonly-adopted metrics. We hope that, the benchmarks, the comparative results, and the open-sourced evaluation code will foster future research in this direction among the 3D object retrieval community.","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"51 1","pages":"104-115"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75572242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A study of deep single sketch-based modeling: View/style invariance, sparsity and latent space disentanglement 基于深度单一草图的建模研究:视图/样式不变性、稀疏性和潜在空间解纠缠

Computer Graphics World Pub Date : 2022-06-01 DOI: 10.2139/ssrn.3999114

Yue Zhong, Yulia Gryaditskaya, Honggang Zhang, Yi-Zhe Song

引用次数: 5

A parallel algorithm for computing Voronoi diagram of a set of spheres using restricted lower envelope approach and topology matching 利用受限下包络法和拓扑匹配计算一组球体Voronoi图的并行算法

Computer Graphics World Pub Date : 2022-06-01 DOI: 10.2139/ssrn.4095671

M. Mukundan, S. Thayyil, Ramanathan Muthuganapathy

引用次数: 1

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data SHREC 2022:使用图像和RGB-D数据检测道路路面的坑洼和裂缝

Computer Graphics World Pub Date : 2022-05-26 DOI: 10.48550/arXiv.2205.13326

E. M. Thompson, A. Ranieri, S. Biasotti, Miguel Chicchón, I. Sipiran, Minh Pham, Thang-Long Nguyen-Ho, Hai-Dang Nguyen, M. Tran