Using neural networks to represent 3D objects has become popular. However, many previous works employ neural networks with fixed architecture and size to represent different 3D objects, which lead to excessive network parameters for simple objects and limited reconstruction accuracy for complex objects. For each 3D model, it is desirable to have an end-to-end neural network with as few parameters as possible to achieve high-fidelity reconstruction. In this paper, we propose an efficient voxel reconstruction method utilizing neural architecture search (NAS) and binary classification. Taking the number of layers, the number of nodes in each layer, and the activation function of each layer as the search space, a specific network architecture can be obtained based on reinforcement learning technology. Furthermore, to get rid of the traditional surface reconstruction algorithms (e.g., marching cube) used after network inference, we complete the end-to-end network by classifying binary voxels. Compared to other signed distance field (SDF) prediction or binary classification networks, our method achieves significantly higher reconstruction accuracy using fewer network parameters.
翻译:使用神经网络来代表 3D 对象已经变得很受欢迎。 但是,许多以前的工作都采用固定结构和大小的神经网络来代表不同的 3D 对象,这导致简单的物体的网络参数过大,复杂物体的重建精确度有限。对于每个 3D 模型,最好有一个端到端神经网络,其参数尽可能少,以达到高不贞的重建。在本文件中,我们建议采用高效的 voxel 重建方法,使用神经结构搜索和二进制分类。以层数、每个层节点的数量以及作为搜索空间的每个层的激活功能,可以根据强化学习技术获得一个特定的网络结构。此外,为了摆脱网络推推后使用的传统地面重建算法(如行进立方块),我们通过对二进毒菌进行分类,完成了终端到端网络。与其他签署的远程域预测或二进分解网络相比,我们的方法使用较少的网络参数,实现了相当高的重建精确度。