Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Jiang, Puhua"'
In this paper, we propose SRIF, a novel Semantic shape Registration framework based on diffusion-based Image morphing and Flow estimation. More concretely, given a pair of extrinsically aligned shapes, we first render them from multi-views, and then
Externí odkaz:
http://arxiv.org/abs/2409.11682
In this paper, we propose a novel learning-based framework for non-rigid point cloud matching, which can be trained purely on point clouds without any correspondence annotation but also be extended naturally to partial-to-full matching. Our key insig
Externí odkaz:
http://arxiv.org/abs/2408.08568
In this paper, we propose a learning-based framework for non-rigid shape registration without correspondence supervision. Traditional shape registration techniques typically rely on correspondences induced by extrinsic proximity, therefore can fail i
Externí odkaz:
http://arxiv.org/abs/2311.04494
Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps within a collection of shapes. In this paper, we investigate its utility in the approaches of Deep Functional Maps, which are considered state-of-the-art in non
Externí odkaz:
http://arxiv.org/abs/2308.08871
As a primitive 3D data representation, point clouds are prevailing in 3D sensing, yet short of intrinsic structural information of the underlying objects. Such discrepancy poses great challenges on directly establishing correspondences between point
Externí odkaz:
http://arxiv.org/abs/2303.01038
Autor:
Wang, Xueyang, Chen, Xuecheng, Jiang, Puhua, Lin, Haozhe, Yuan, Xiaoyun, Ji, Mengqi, Guo, Yuchen, Huang, Ruqi, Fang, Lu
Publikováno v:
In Engineering March 2024 34:70-82
