no code implementations • 8 Dec 2020 • Xinhai Liu, Xinchen Liu, Yu-Shen Liu, Zhizhong Han
The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets.
1 code implementation • 26 May 2020 • Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker
According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category.
no code implementations • 18 Mar 2020 • Xinhai Liu, Zhizhong Han, Fangzhou Hong, Yu-Shen Liu, Matthias Zwicker
However, due to the irregularity and sparsity in sampled point clouds, it is hard to encode the fine-grained geometry of local regions and their spatial relationships when only using the fixed-size filters and individual local feature integration, which limit the ability to learn discriminative features.
no code implementations • 29 Aug 2019 • Xin Wen, Zhizhong Han, Xinhai Liu, Yu-Shen Liu
Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters.
no code implementations • 2 Aug 2019 • Xinhai Liu, Zhizhong Han, Xin Wen, Yu-Shen Liu, Matthias Zwicker
Specifically, L2G-AE employs an encoder to encode the geometry information of multiple scales in a local region at the same time.
no code implementations • 18 May 2019 • Zhizhong Han, Xinhai Liu, Yu-Shen Liu, Matthias Zwicker
In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views.
no code implementations • 6 Nov 2018 • Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker
However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features.
Ranked #35 on
3D Part Segmentation
on ShapeNet-Part