Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Saroha, Abhishek"'
Neural implicit surfaces can be used to recover accurate 3D geometry from imperfect point clouds. In this work, we show that state-of-the-art techniques work by minimizing an approximation of a one-sided Chamfer distance. This shape metric is not sym
Externí odkaz:
http://arxiv.org/abs/2407.17058
Autor:
Saroha, Abhishek, Gladkova, Mariia, Curreli, Cecilia, Muhle, Dominik, Yenamandra, Tarun, Cremers, Daniel
3D scene stylization extends the work of neural style transfer to 3D. A vital challenge in this problem is to maintain the uniformity of the stylized appearance across multiple views. A vast majority of the previous works achieve this by training a 3
Externí odkaz:
http://arxiv.org/abs/2403.08498
It is by now a well known fact in the graph learning community that the presence of bottlenecks severely limits the ability of graph neural networks to propagate information over long distances. What so far has not been appreciated is that, counter-i
Externí odkaz:
http://arxiv.org/abs/2310.00431
Neural implicit representations have become a popular choice for modeling surfaces due to their adaptability in resolution and support for complex topology. While previous works have achieved impressive reconstruction quality by training on ground tr
Externí odkaz:
http://arxiv.org/abs/2306.02099
We present a novel neural implicit shape method for partial point cloud completion. To that end, we combine a conditional Deep-SDF architecture with learned, adversarial shape priors. More specifically, our network converts partial inputs into a glob
Externí odkaz:
http://arxiv.org/abs/2204.10060
Histogram Equalization (HE) is a popular method for contrast enhancement. Generally, mean brightness is not conserved in Histogram Equalization. Initially, Bi-Histogram Equalization (BBHE) was proposed to enhance contrast while maintaining a the mean
Externí odkaz:
http://arxiv.org/abs/2003.00840
The structure of the network has great impact on its traffic dynamics. Most of the real world networks follow the heterogeneous structure and exhibit scale-free feature. In scale-free network, a new node prefers to connect with hub nodes and the netw
Externí odkaz:
http://arxiv.org/abs/1811.06261
Publikováno v:
In Physica A: Statistical Mechanics and its Applications 1 May 2020 545
Neural surface implicit representations offer numerous advantages, including the ability to easily modify topology and surface resolution. However, reconstructing implicit geometry representation with only limited known data is challenging. In this p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1a53c29479674c669e7ecc527a739af