Deep Learning with Sets and Point Clouds
We introduce a simple permutation equivariant layer for deep learning with set structure.This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep permutation-invariant networks to perform point-could classification and MNIST-digit summation, where in both cases the output is invariant to permutations of the input. In a semi-supervised setting, where the goal is make predictions for each instance within a set, we demonstrate the usefulness of this type of layer in set-outlier detection as well as semi-supervised learning with clustering side-information.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDeep LearningGeneral ClassificationOutlier DetectionSimilar Papers 제목 키워드 기반
Efficient Point Clouds Upsampling via Flow Matching
Diffusion models are a powerful framework for tackling ill-posed problems, with recent advancements extending their use to point cloud upsampling. Despite their potential, existing diffusion models struggle with ineffici…
point cloud upsamplingPre-Training by Completing Point Clouds
There has recently been a flurry of exciting advances in deep learning models on point clouds. However, these advances have been hampered by the difficulty of creating labelled point cloud datasets: sparse point clouds o…
CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo
SegContrast paved the way for contrastive learning on outdoor point clouds. Its original formulation targeted individual scans in applications like autonomous driving and object detection. However, mobile mapping purpose…
3D Semantic SegmentationAutonomous DrivingContrastive LearningData Augmentation+5A Point Cloud-Based Deep Learning Strategy for Protein-Ligand Binding Affinity Prediction
There is great interest to develop artificial intelligence-based protein-ligand affinity models due to their immense applications in drug discovery. In this paper, PointNet and PointTransformer, two pointwise multi-layer…
Drug DiscoveryProtein-Ligand Affinity PredictionPoint-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Clouds
Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving. However, labeling point cloud data is labor-intensive as it requires human annotators to visualize and…
Autonomous Drivingdomain classificationObject