Recently, several works have addressed modeling of 3D shapes using deep neural networks to learn implicit surface representations. Up to now, the majority of works have concentrated on reconstruction quality, paying little or no attention to model size or training time. This work proposes LightSAL, a novel deep convolutional architecture for learning 3D shapes; the proposed work concentrates on efficiency both in network training time and resulting model size. We build on the recent concept of Sign Agnostic Learning for training the proposed network, relying on signed distance fields, with unsigned distance as ground truth. In the experimental section of the paper, we demonstrate that the proposed architecture outperforms previous work in model size and number of required training iterations, while achieving equivalent accuracy. Experiments are based on the D-Faust dataset that contains 41k 3D scans of human shapes. The proposed model has been implemented in PyTorch.
翻译:最近,有几部作品利用深层神经网络对3D形状进行建模,以学习隐含表面表现。到目前为止,大多数作品都集中在重建质量上,很少或完全不注意模型大小或培训时间。这项工作提出了LightSAL,这是一个用于学习3D形状的新的深层革命结构;拟议中的工作侧重于网络培训时间和由此形成的模型大小的效率。我们以最近的信号Agnistic Learning概念为基础,对拟议的网络进行培训,依靠已签字的距离字段,将未指派的距离作为地面真相。在论文的实验部分,我们证明拟议的结构在模型大小和所需培训迭代数方面超过了以前的工作,同时实现了同等的准确性。实验以D-Faust数据集为基础,该数据集包含41k 3D人类形状的扫描。拟议的模型已在PyTorch实施。