Search Results for author: Xinhai Liu

Found 7 papers, 1 papers with code

SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization

no code implementations8 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.

Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions

1 code implementation26 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.

3D Shape Classification General Classification +2

LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts

no code implementations18 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.

Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules

no code implementations29 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.

L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention

no code implementations2 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.

Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views

no code implementations18 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.

Region Proposal

Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

no code implementations6 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.

3D Part Segmentation 3D Point Cloud Classification +1

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