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Found 8 papers, 5 papers with code
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Besides, such scenes with progressive masking ratios can also serve to self-distill their intrinsic spatial consistency, requiring to learn the consistent representations from unmasked areas.
CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
Inspired by prompting approaches from NLP, we creatively reinterpret point cloud generation and refinement as the prompting and predicting stages, respectively.
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning
To fill this gap, we propose a generic Contour-Perturbed Reconstruction Network (CP-Net), which can effectively guide self-supervised reconstruction to learn semantic content in the point cloud, and thus promote discriminative power of point cloud representation.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud
This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds.
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components.
Ranked #1 on
Point Cloud Segmentation
on PointCloud-C
Geometry Sharing Network for 3D Point Cloud Classification and Segmentation
Specially, GS-Net consists of Geometry Similarity Connection (GSC) modules which exploit Eigen-Graph to group distant points with similar and relevant geometric information, and aggregate features from nearest neighbors in both Euclidean space and Eigenvalue space.
Ranked #7 on
3D Point Cloud Classification
on IntrA
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds.
Ranked #6 on
3D Part Segmentation
on IntrA
