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Shrec10

Web针对传统的三维模型单一特征识别精度低问题,提出一种自适应稀疏编码融合的非刚性三维模型分类算法.首先提取模型的平均测地线特征、热核特征、形状直径函数特征,以构造互补的多特征形状描述;其次利用特征袋模型构造AGD-BoF, HKS-BoF, SDF-BoF特征向量,通过随机样本建立特征权值矩阵;最后利用特征 ... Web使用python调用VTK库对网格的输入特征进行可视化，方便后续实验与分析。

SHREC 2024: Large-scale 3D Shape Retrieval from ShapeNet Core55

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OptimalDiffusionKernels/SHREC10_SCRIPT.m at master · rlit ...

WebMavridis et al., / SHREC’10 Track: Protein Models 1tteA02 2vrnA00 1su1A00 2o04A00 1brtA00 3c2bA02 2a1uA01 1dbhA02 1t9bA03 1w9cA00 2zmfA00 1wwjA00 2gnnB00 1c9bA01 1n27A00 2pw9A02 Web*Both for shrec16 and shrec10, the results reported in the paper are averaged over the 3 splits (e.g., for shrec10, config B, (100. + 99.67 + 99.67) / 3 = 99.78%). Retraining To rerun … WebThe SHREC10 dataset includes 200 non-rigid 3D mesh models in 10 categories. There are 20 models in each category and the same models have rigid body transformation and non … doctrine of strict product liability

SHREC10 Track: Protein Model Classification - uni-freiburg.de

Category:A Convolutional Vector Network for 3D Mesh Object Recognition

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Shrec10

Skeleton Embedded Views for Non-Rigid 3D Shape Classification …

WebNov 1, 2016 · SHREC10 [7] contains 1184 shapes of 13 classes in total. For each class, there are 10 different transformations, including isometry, topology, small and big holes, global and local scaling, noise, shot noise, partial occlusion, sampling, and a combination of 5 different levels of transformations. Thus there are 56 shapes for each class, 728 ... Web関連論文リスト. HAM: Hierarchical Attention Model with High Performance for 3D Visual Grounding [30.40075685188971] 本稿では,ポイントクラウド上の3次元視覚的グラウンド化という,新たな視覚言語課題に取り組む。

Shrec10

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DataSet for SHREC 2010 - Shape Retrieval Contest of Range Scans The participants can choose the high resolution or the low resolution version of the "Range Query Dataset" whichever suits their methods. Download the Sample Range Query DataSet in high resolution WebComparison of the precision Recall curves (PRcurves) among our method and the other methods on SHREC10 Non-rigid dataset. Source publication Non-rigid 3D shape retrieval based on multi-view...

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WebSHREC'10 Track: Range Scan Retrieval - NIST Information ... WebSep 5, 2024 · It learns deep features from intrinsic feature images and extrinsic 2D views, and generates a synthetic feature vector via weighted aggregation and refinement process, which has achieved remarkable improvement in non-rigid 3D shape classification.

Web通过在非刚性数据集shrec10与shrec11上的综合实验表明, 自适应稀疏编码融合的分类算法具有更高的识别准确率及较强的鲁棒性. [特征提取, 特征袋模型, 稀疏编码, 形状分类, 非刚性三维模型] [韩丽, 刘书宁, 徐圣斯, 朴京钰]

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