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Generation For Adaption: A GAN-Based Approach for 3D Domain Adaption with Point Cloud Data

arXiv 2102.07373 ↗First posted 15 Feb 2021cs.CVcited by 0

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Abstract (arXiv)

Recent deep networks have achieved good performance on a variety of 3d points classification tasks. However, these models often face challenges in "wild tasks".There are considerable differences between the labeled training/source data collected by one Lidar and unseen test/target data collected by a different Lidar. Unsupervised domain adaptation (UDA) seeks to overcome such a problem without target domain labels.Instead of aligning features between source data and target data,we propose a method that use a Generative adversarial network to generate synthetic data from the source domain so that the output is close to the target domain.Experiments show that our approach performs better than other state-of-the-art UDA methods in three popular 3D object/scene datasets (i.e., ModelNet, ShapeNet and ScanNet) for cross-domain 3D objects classification.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 15 Feb 2021.

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30 of 46 references.

References · 30

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  5. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  6. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  7. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  8. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  9. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
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  11. 2016Least Squares Generative Adversarial Networkscited by 1,000
  12. 2020PCT: Point cloud transformercited by 730
18 more
  1. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 682
  2. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  3. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
  4. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  5. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 307
  6. 2016Learning from Simulated and Unsupervised Images through Adversarial Trainingcited by 287
  7. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  8. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
  9. 2018Point Convolutional Neural Networks by Extension Operatorscited by 154
  10. 2019PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representationcited by 114
  11. 2018MeshNet: Mesh Neural Network for 3D Shape Representationcited by 95
  12. 2019Unpaired Point Cloud Completion on Real Scans using Adversarial Trainingcited by 66
  13. 2018Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Cloudscited by 62
  14. 2020Multimodal Shape Completion via Conditional Generative Adversarial Networkscited by 61
  15. 2017Dual Discriminator Generative Adversarial Netscited by 55
  16. 2018PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognitioncited by 36
  17. 2019Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Datacited by 35
  18. 2019Generative Modeling for Small-Data Object Detectioncited by 20

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