SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
University of Cambridge（剑桥大学）
英文摘要：
We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic segmentation is built on a deep autoencoder stack trained exclusively on synthetic depth data generated from our novel 3D scene library, SynthCam3D. Importantly, our network is able to segment real world scenes without any noise modelling. We present encouraging preliminary results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
