基于 ShapeNet Core55 的大规模三维形状重建与分割
计算机视觉与模式识别
2017-10-31 v2
摘要
我们使用来自 ShapeNet 三维物体数据库的数据和标注,引入了一个大规模三维形状理解基准。该基准包含两项任务:三维形状的部件级分割和从单视图图像进行三维重建。十支团队参与了该挑战赛,表现最好的团队在两项任务上均超越了 SOTA 方法。在两项任务的各种三维表示上,提出了若干新颖的深度学习架构。我们报告了各团队使用的技术及其相应性能。此外,我们总结了从报告结果中得出的主要发现以及该领域未来工作的可能趋势。
引用
@article{arxiv.1710.06104,
title = {Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55},
author = {Li Yi and Lin Shao and Manolis Savva and Haibin Huang and Yang Zhou and Qirui Wang and Benjamin Graham and Martin Engelcke and Roman Klokov and Victor Lempitsky and Yuan Gan and Pengyu Wang and Kun Liu and Fenggen Yu and Panpan Shui and Bingyang Hu and Yan Zhang and Yangyan Li and Rui Bu and Mingchao Sun and Wei Wu and Minki Jeong and Jaehoon Choi and Changick Kim and Angom Geetchandra and Narasimha Murthy and Bhargava Ramu and Bharadwaj Manda and M Ramanathan and Gautam Kumar and P Preetham and Siddharth Srivastava and Swati Bhugra and Brejesh Lall and Christian Haene and Shubham Tulsiani and Jitendra Malik and Jared Lafer and Ramsey Jones and Siyuan Li and Jie Lu and Shi Jin and Jingyi Yu and Qixing Huang and Evangelos Kalogerakis and Silvio Savarese and Pat Hanrahan and Thomas Funkhouser and Hao Su and Leonidas Guibas},
journal= {arXiv preprint arXiv:1710.06104},
year = {2017}
}