论文标题
具有输出引导的跳过连接的深度OCTREE CNN，以进行3D形状和场景完成
Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion
论文作者
论文摘要
由于几何阻塞和3D捕获期间的视图不足，因此获得完整而干净的3D形状和场景数据是具有挑战性的。我们提出了一种简单而有效的深度学习方法，用于完成输入嘈杂和不完整的形状或场景。我们的网络建立在基于OCTREE的CNN（O-CNN）上，具有U-NET，例如结构，该结构具有较高的计算和内存效率，并支持为3D CNN构建非常深的网络结构。将新型的输出引导的跳过连接引入了网络结构，以更好地保留输入几何并有效地从数据中保存几何形状。我们表明，借助这些简单的改编 - 输出引导的跳过连接和更深的O-CNN（最高70层），我们的网络实现了最新的最新结果，从而可以完成3D形状的完成和语义场景计算。
Acquiring complete and clean 3D shape and scene data is challenging due to geometric occlusion and insufficient views during 3D capturing. We present a simple yet effective deep learning approach for completing the input noisy and incomplete shapes or scenes. Our network is built upon the octree-based CNNs (O-CNN) with U-Net like structures, which enjoys high computational and memory efficiency and supports to construct a very deep network structure for 3D CNNs. A novel output-guided skip-connection is introduced to the network structure for better preserving the input geometry and learning geometry prior from data effectively. We show that with these simple adaptions -- output-guided skip-connection and deeper O-CNN (up to 70 layers), our network achieves state-of-the-art results in 3D shape completion and semantic scene computation.
