Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Li, Manyi"'
Point cloud understanding is an inherently challenging problem because of the sparse and unordered structure of the point cloud in the 3D space. Recently, Contrastive Vision-Language Pre-training (CLIP) based point cloud classification model i.e. Poi
Externí odkaz:
http://arxiv.org/abs/2408.03545
Deep generative models learn the data distribution, which is concentrated on a low-dimensional manifold. The geometric analysis of distribution transformation provides a better understanding of data structure and enables a variety of applications. In
Externí odkaz:
http://arxiv.org/abs/2406.18588
2D irregular shape packing is a necessary step to arrange UV patches of a 3D model within a texture atlas for memory-efficient appearance rendering in computer graphics. Being a joint, combinatorial decision-making problem involving all patch positio
Externí odkaz:
http://arxiv.org/abs/2309.10329
How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of
Externí odkaz:
http://arxiv.org/abs/2309.08942
Autor:
Patil, Akshay Gadi, Patil, Supriya Gadi, Li, Manyi, Fisher, Matthew, Savva, Manolis, Zhang, Hao
This report surveys advances in deep learning-based modeling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various in
Externí odkaz:
http://arxiv.org/abs/2304.03188
Publikováno v:
In AEUE - International Journal of Electronics and Communications November 2024 186
Publikováno v:
In AEUE - International Journal of Electronics and Communications September 2024 184
Autor:
Dong, Qiujie, Wang, Zixiong, Li, Manyi, Gao, Junjie, Chen, Shuangmin, Shu, Zhenyu, Xin, Shiqing, Tu, Changhe, Wang, Wenping
Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation. When the input is a polygonal surface, one has to suffer from the irregular mesh
Externí odkaz:
http://arxiv.org/abs/2202.00307
We introduce RIM-Net, a neural network which learns recursive implicit fields for unsupervised inference of hierarchical shape structures. Our network recursively decomposes an input 3D shape into two parts, resulting in a binary tree hierarchy. Each
Externí odkaz:
http://arxiv.org/abs/2201.12763
Autor:
Yu, Fenggen, Chen, Zhiqin, Li, Manyi, Sanghi, Aditya, Shayani, Hooman, Mahdavi-Amiri, Ali, Zhang, Hao
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies. Our network takes an input 3D shape that can be provided
Externí odkaz:
http://arxiv.org/abs/2104.05652