3D Semantic Scene Completion from a Single Depth Image Using Adversarial Training
3D Semantic Scene Completion from a Single Depth Image Using Adversarial Training
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DOI：
10.1109/icip.2019.8803174
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发表时间：
2019-05
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通讯作者：
Yueh-Tung Chen;Martin Garbade;Juergen Gall
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文献类型：
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作者：
Yueh-Tung Chen;Martin Garbade;Juergen Gall
我们解决了3D语义场景完成的任务，即，给定单个深度图像，我们预测表示场景的3D网格中的体素的语义标签和占用。鉴于最近引入的生成对抗网络（GAN），我们的目标是探索这种模型的潜力和各种重要设计选择的效率。我们的结果表明，使用条件GAN的性能优于普通GAN设置。我们在几个数据集上评估这些架构设计。基于我们的实验，我们证明了GAN在干净注释的情况下能够优于基线3D CNN的性能，但它们受到注释对齐不良的影响。
We address the task of 3D semantic scene completion, i.e., given a single depth image, we predict the semantic labels and occupancy of voxels in a 3D grid representing the scene. In light of the recently introduced generative adversarial networks (GAN), our goal is to explore the potential of this model and the efficiency of various important design choices. Our results show that using conditional GANs outperforms the vanilla GAN setup. We evaluate these architecture designs on several datasets. Based on our experiments, we demonstrate that GANs are able to outperform the performance of a baseline 3D CNN in case of clean annotations, but they suffer from poorly aligned annotations.