Open AccessProceedings Article
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Charles R. Qi,Li Yi,Hao Su,Leonidas J. Guibas +3 more
- Vol. 30, pp 5099-5108
Reads0
Chats0
TLDR
PointNet++ as discussed by the authors applies PointNet recursively on a nested partitioning of the input point set to learn local features with increasing contextual scales, and proposes novel set learning layers to adaptively combine features from multiple scales.Abstract:
Few prior works study deep learning on point sets. PointNet is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.read more
Citations
More filters
Posted Content
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features.
TL;DR: This paper proposes a linked dynamic graph CNN (LDGCNN) to classify and segment point cloud directly and shows that the proposed LDGCNN achieves state-of-art performance on two standard datasets: ModelNet40 and ShapeNet.
Book ChapterDOI
PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation
TL;DR: A novel end-to-end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse- to-fine fashion, and shows great generalization ability on the KITTI Scene Flow 2015 dataset, outperforming all previous methods.
Book ChapterDOI
Deep FusionNet for Point Cloud Semantic Segmentation
TL;DR: The proposed deep fusion network architecture (FusionNet) with a unique voxel-based “mini-PointNet” point cloud representation and a new feature aggregation module (fusion module) for large-scale 3D semantic segmentation can learn more accurate point-wise predictions when compared to voxels-based convolutional networks.
Proceedings ArticleDOI
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion
TL;DR: In this paper, RL-GAN-Net is applied to point cloud shape completion that converts noisy, partial point cloud data into a high-fidelity completed shape by controlling the GAN.
Proceedings ArticleDOI
PointFlowNet: Learning Representations for Rigid Motion Estimation From Point Clouds
TL;DR: In this article, a deep neural network is proposed to estimate 3D scene flow from unstructured point clouds using a single forward pass, which jointly predicts 3D bounding box and rigid body motion of objects in the scene.