Abstract

3D-R2N2 and other advanced 3D reconstruction neural networks have achieved impressive results, however most of them still suffer from training difficulties and detail losing, due to their weak feature extraction capability and improper loss function. This paper aims to overcome these shortcomings and defects by building a brand new model based on 3D-R2N2. The new model adopts densely connected structure as encoder, and utilizes Chamfer Distance as loss function. The aim is to enhance the learning ability of the network for complex data, meanwhile, make the focus of the whole network rest on the reconstruction of detail structures. In addition, we also made an improved decoder by building two parallel predictor branches to make better use of the feature information and boost the network’s performance on reconstruction task. Through extensive tests, the results show that our proposed model called 3D-R2N2-V2 is slightly slower than 3D-R2N2 in predicting speed, but it can be 20% to 30% faster than 3D-R2N2 in training speed and obtain 15% and 10% better voxel IoU results on both single- and multi-view reconstruction tasks, respectively. Compared with other recent state-of-the-art methods like OGN and DRC, the reconstruction effect of our approach is also competitive.

中文翻译：

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改进的递归神经网络用于3D对象重建

摘要

3D-R2N2和其他先进的3D重建神经网络已经取得了令人印象深刻的结果，但是由于它们的特征提取能力较弱和丢失功能不正确，它们中的大多数仍然遭受训练困难和细节丢失的困扰。本文旨在通过构建基于3D-R2N2的全新模型来克服这些缺点和缺陷。新模型采用紧密连接的结构作为编码器，并利用倒角距离作为损耗函数。目的是增强网络对复杂数据的学习能力，同时使整个网络的重点放在细节结构的重建上。此外，我们还通过构建两个并行的预测器分支来改进解码器，以更好地利用特征信息并提高网络在重建任务上的性能。通过广泛的测试，结果表明，我们提出的称为3D-R2N2-V2的模型在预测速度上稍慢于3D-R2N2，但在训练速度上可比3D-R2N2快20％至30％，并获得了15％和10％的更好的体素IoU分别在单视图和多视图重建任务上产生结果。与OGN和DRC等其他最新技术相比，我们的方法的重建效果具有竞争优势。