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.

用深度学习方法，采用 O-CNN 和 U-Net 结构，在保留输入几何形状和从数据中学习几何形状之前，通过引入新的输出引导跳过连接的网络结构对输入的嘈杂和不完整形状或场景进行了完整性处理，并在三维形状完整性和语义场景计算方面取得了最先进的结果。

基于深度八叉树卷积神经网络的输出引导跳跃连接用于 3D 形状和场景完整性

Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape  and Scene Completion

We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D
shape analysis. Built upon the octree representation of 3D shapes, our method
takes the average normal vectors of a 3D model sampled in the finest leaf
octants as input and performs 3D CNN operations on the octants occupied by the
3D shape surface. We design a novel octree data structure to efficiently store
the octant information and CNN features into the graphics memory and execute
the entire O-CNN training and evaluation on the GPU. O-CNN supports various CNN
structures and works for 3D shapes in different representations. By restraining
the computations on the octants occupied by 3D surfaces, the memory and
computational costs of the O-CNN grow quadratically as the depth of the octree
increases, which makes the 3D CNN feasible for high-resolution 3D models. We
compare the performance of the O-CNN with other existing 3D CNN solutions and
demonstrate the efficiency and efficacy of O-CNN in three shape analysis tasks,
including object classification, shape retrieval, and shape segmentation.

O-CNN 是一种基于八叉树结构的 3D 形状分析卷积神经网络，通过将 3D 模型的平均法向量作为输入并在被 3D 形状表面占用的八叉树执行 3D 卷积操作，在 GPU 上高效执行整个网络的训练和评估，在物体分类、形状检索和形状分割等三个任务上显示出高效和有效性。