We present CIRCLE, a framework for large-scale scene completion and geometric refinement based on local implicit signed distance functions. It is based on an end-to-end sparse convolutional network, CircNet, that jointly models local geometric details and global scene structural contexts, allowing it to preserve fine-grained object detail while recovering missing regions commonly arising in traditional 3D scene data. A novel differentiable rendering module enables test-time refinement for better reconstruction quality. Extensive experiments on both real-world and synthetic datasets show that our concise framework is efficient and effective, achieving better reconstruction quality than the closest competitor while being 10-50x faster.

翻译：我们介绍了基于当地隐含签名的远程功能的大规模现场完成和几何改进框架CIRCLE,它基于一个端到端的稀有革命网络CircNet,共同模拟当地几何细节和全球场景结构背景,使其能够保存细微的天体细节,同时恢复传统3D现场数据中常见的失踪区域。一个新颖的、不同的制作模块可以测试时间的改进,以提高重建质量。 有关真实世界和合成数据集的广泛实验表明,我们的简明框架是高效和有效的,比最近的竞争对手更快地实现更好的重建质量。