Consequently, a compact and informative subset of priors can be learned to efficiently encode all shapes of the same family. A comprehensive library of local shape priors is first built with the given collect...
We use an L 1-type prior that enables us to adaptively compute sparse and low dimensional parameterization of deformations. We show an application of our method for comparing anatomical shapes of patients with Down’s syndrome and healthy controls, where the sparse parametrization of diffeomorphisms ...
These 3D data types are mostly point clouds, meshes, and voxel grids, which are the focus of a wide range of applications, especially in robotics. First, we shortly present the basic autoencoder with the extensions towards the VAE with further subcategories relevant to discrete point cloud ...
Benefited from learning directly in raw point clouds, our method is also able to precisely estimate 3D bound- ing boxes even under strong occlusion or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection benchmarks, our method outperforms the state of the art by remarkable...
64 7.33 Large-scale Study Of Curiosity-driven Learning 6, 9, 7 1.25 Accept (Poster) 65 7.33 Learning Localized Generative Models For 3d Point Clouds Via Graph Convolution 9, 6, 7 1.25 Accept (Poster) 66 7.33 Prior Convictions: Black-box Adversarial Attacks With Bandits And Priors 7, 8, ...
Sparse conditional copula models for structured output regression. Minyoung Kim 原文链接 谷歌学术 必应学术 百度学术 Hypergraph modelling for geometric model fitting. Guobao XiaoHanzi WangTaotao LaiDavid Suter 原文链接 谷歌学术 必应学术 百度学术 Data-driven techniques for smoothing histograms of local binary...
The model-driven methods are robust when reconstructing buildings as prior knowledge, such as parallel and symmetry, can be easily combined to create a watertight geometric model of buildings. However, roof shapes in the physical world are diverse, and these methods fail when a to-be-processed ...
Furthermore, traditional display methods often involve images or 3D models, whereas big data and AI algorithm driven methods can incorporate videos or interactions, resulting in more intuitive and engaging displays. Evaluation is the final step. Once the product is designed, it will be presented to...
IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos. Kihyuk Sohn Sifei Liu Guangyu Zhong Xiang Yu Ming-Hsuan Yang Manmohan Chandraker 电商所评分:6 ...
NonSmooth Variational Data Assimilation with Sparse Priors. Ebtehaj A M,Foufoula-Georgiou E,Zhang S Q,et al. https:∥www.researchgate.net/publication/228096140 . 2012Ebtehaj A M, Foufoula-Georgiou E, Zhang S Q, et al . Non-Smooth Variational Data Assimilation with Sparse Priors[J/OL].2012...