论文标题
3D-NVS：下一个视图选择的3D监督方法
3D-NVS: A 3D Supervision Approach for Next View Selection
论文作者
论文摘要
我们为下一个最佳视图选择提供了一种基于分类的方法，并展示了如何合理地获得此任务的监督信号。拟议的方法是端到端的训练，旨在通过一对被动获得的2D视图获得最佳的3D重建质量。所提出的模型由两个阶段组成：分类器和一个通过地面真实体素的间接3D监督共同训练的分类器和重建网络。在测试时，提出的方法没有对基础3D形状进行选择的先验知识，以选择下一个最佳视图。我们通过对合成和真实图像的详细实验证明了该方法的有效性，并展示了与现有的3D重建状态和下一个最佳视图预测技术相比，它如何提供改进的重建质量。
We present a classification based approach for the next best view selection and show how we can plausibly obtain a supervisory signal for this task. The proposed approach is end-to-end trainable and aims to get the best possible 3D reconstruction quality with a pair of passively acquired 2D views. The proposed model consists of two stages: a classifier and a reconstructor network trained jointly via the indirect 3D supervision from ground truth voxels. While testing, the proposed method assumes no prior knowledge of the underlying 3D shape for selecting the next best view. We demonstrate the proposed method's effectiveness via detailed experiments on synthetic and real images and show how it provides improved reconstruction quality than the existing state of the art 3D reconstruction and the next best view prediction techniques.
