SL3D: Self-supervised-Self-labeled 3D Recognition
Not on the map. Why?
Abstract (arXiv)
Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of these results rely on manually collecting densely annotated real-world 3D data, which is highly time-consuming and expensive to obtain, limiting the scalability of 3D recognition tasks. Thus, we study unsupervised 3D recognition and propose a Self-supervised-Self-Labeled 3D Recognition (SL3D) framework. SL3D simultaneously solves two coupled objectives, i.e., clustering and learning feature representation to generate pseudo-labeled data for unsupervised 3D recognition. SL3D is a generic framework and can be applied to solve different 3D recognition tasks, including classification, object detection, and semantic segmentation. Extensive experiments demonstrate its effectiveness. Code is available at https://github.com/fcendra/sl3d.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 45 of its 50 references that are in the map. First posted on arXiv 30 Oct 2022.
Builds on
45 of 50 references.
References · 45
- 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
- 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
- 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
- 2020Improved Baselines with Momentum Contrastive Learningcited by 2,287
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
- 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
- 2020Point Transformercited by 1,470
- 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
- 2016Colorful Image Colorizationcited by 1,114
33 more
- 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
- 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
- 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 748
- 2020PCT: Point cloud transformercited by 732
- 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
- 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
- 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
- 2019Self-labelling via simultaneous clustering and representation learningcited by 528
- 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
- 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 504
- 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 455
- 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
- 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
- 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
- 2017Colorization as a Proxy Task for Visual Understandingcited by 175
- 2018GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloudcited by 146
- 2021Mix3D: Out-of-Context Data Augmentation for 3D Scenescited by 126
- 2017Representation Learning by Learning to Countcited by 120
- 2021Bidirectional Projection Network for Cross Dimension Scene Understandingcited by 95
- 2018Boosting Self-Supervised Learning via Knowledge Transfercited by 92
- 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
- 2021Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentationcited by 87
- 2020Self-supervised Learning of Point Clouds via Orientation Estimationcited by 72
- 2020Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Cloudscited by 71
- 2020Weakly Supervised 3D Object Detection from Lidar Point Cloudcited by 67
- 2020JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Cloudscited by 62
- 2018Self-Supervised Feature Learning by Learning to Spot Artifactscited by 43
- 2022RBGNet: Ray-based Grouping for 3D Object Detectioncited by 40
- 20213D Spatial Recognition without Spatially Labeled 3Dcited by 31
- 2022Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancementcited by 17
- 2020PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervisioncited by 15
- 2019MortonNet: Self-Supervised Learning of Local Features in 3D Point Cloudscited by 9
- 2018SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projectioncited by 8
Built on by
Citing papers per year: 2023: 2; 2024: 0; 2025: 1.
3 papers.