论文标题
从Wasserstein Gan和Transformers的几个点完成点云
Completing point cloud from few points by Wasserstein GAN and Transformers
论文作者
论文摘要
在许多视觉和机器人应用中，常见的是，捕获的对象以很少的观点表示。大多数现有的完成方法都是针对具有许多点的部分点云而设计的，并且在几个点的情况下，它们的性能差甚至完全失败。但是，由于缺乏详细信息，从几个点完成对象面临巨大的挑战。受GAN和Transformers在基于图像的视觉任务中的成功应用的启发，我们介绍了GAN和Transformer技术以解决上述问题。首先，带有变压器和带有变压器的Wasserstein GAN的端到端编码器网络已进行了预训练，然后整体网络进行了微调。 Shapenet数据集上的实验结果表明，我们的方法不仅可以提高许多输入点的完成性能，而且还可以保持稳定的几个输入点。我们的源代码可在https://github.com/wxfqjh/stability-point-recovery.git上找到。
In many vision and robotics applications, it is common that the captured objects are represented by very few points. Most of the existing completion methods are designed for partial point clouds with many points, and they perform poorly or even fail completely in the case of few points. However, due to the lack of detail information, completing objects from few points faces a huge challenge. Inspired by the successful applications of GAN and Transformers in the image-based vision task, we introduce GAN and Transformer techniques to address the above problem. Firstly, the end-to-end encoder-decoder network with Transformers and the Wasserstein GAN with Transformer are pre-trained, and then the overall network is fine-tuned. Experimental results on the ShapeNet dataset show that our method can not only improve the completion performance for many input points, but also keep stable for few input points. Our source code is available at https://github.com/WxfQjh/Stability-point-recovery.git.
