Search Results for author:
Found 7 papers, 1 papers with code
SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization
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
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.
LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts
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
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
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
In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views.
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
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 #37 on
3D Part Segmentation
on ShapeNet-Part
