Generation For Adaption: A GAN-Based Approach for 3D Domain Adaption with Point Cloud Data
This addresses domain adaptation for 3D point cloud data, which is incremental as it applies a known GAN approach to a specific bottleneck in cross-domain tasks.
The paper tackles the problem of domain shift in 3D point cloud classification by proposing a GAN-based method to generate synthetic data from the source domain that resembles the target domain, achieving better performance than state-of-the-art unsupervised domain adaptation methods on three datasets.
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.