Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Jena, Shubhendu"'
This paper presents a novel approach for sparse 3D reconstruction by leveraging the expressive power of Neural Radiance Fields (NeRFs) and fast transfer of their features to learn accurate occupancy fields. Existing 3D reconstruction methods from spa
Externí odkaz:
http://arxiv.org/abs/2408.14724
We revisit NPBG, the popular approach to novel view synthesis that introduced the ubiquitous point feature neural rendering paradigm. We are interested in particular in data-efficient learning with fast view synthesis. We achieve this through a view-
Externí odkaz:
http://arxiv.org/abs/2208.05785
We propose to improve on graph convolution based approaches for human shape and pose estimation from monocular input, using pixel-aligned local image features. Given a single input color image, existing graph convolutional network (GCN) based techniq
Externí odkaz:
http://arxiv.org/abs/2111.05319
Computer Vision – ECCV 2022 Workshops : Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III
The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, durin