We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction and auto-encoding. Our method encodes the volumetric field of a 3D shape with an adaptive feature volume organized by an octree and applies a compact multilayer perceptron network for mapping the features to the field value at each 3D position. An encoder-decoder network is designed to learn the adaptive feature volume based on the graph convolutions over the dual graph of octree nodes. The core of our network is a new graph convolution operator defined over a regular grid of features fused from irregular neighboring octree nodes at different levels, which not only reduces the computational and memory cost of the convolutions over irregular neighboring octree nodes, but also improves the performance of feature learning. Our method effectively encodes shape details, enables fast 3D shape reconstruction, and exhibits good generality for modeling 3D shapes out of training categories. We evaluate our method on a set of reconstruction tasks of 3D shapes and scenes and validate its superiority over other existing approaches. Our code, data, and trained models are available at https://wang-ps.github.io/dualocnn.
翻译:我们展示了3D形状的适应性深体体积字段, 并展示了一种有效的方法, 以学习高品质 3D 形状重建与自动编码的深体体体积。 我们的方法是将3D 形状的体积域编码成一个由八叶树组织的适应性特点体积体积, 并应用一个紧凑的多层光谱网络来绘制每个 3D 位置的外观特征。 一个编码- 解码器网络的设计是为了学习基于octree 结点双形图的图解变化的适应性功能体积。 我们网络的核心是一个新的图解变操作器, 在一个固定的由不同级别的非常规相邻八叶节点组成的功能网格上定义, 这不仅能减少环形体积的计算和记忆成本, 而且还能改善地貌学习的性能。 我们的方法有效地塑造了细节, 能够快速的 3D 形状重建, 并展示了3D 形状的模型在培训类别中的典型。 我们评估了一套重建模式的方法, 3D 形状和图像/图像/ 验证了其他的优势。