结合颜色和深度的语义场景补全：初步实验
Semantic Scene Completion Combining Colour and Depth: preliminary experiments
摘要：
语义场景完成是从单视图观察产生具有场景的语义标签的体积占用的完整3D体素表示的任务。我们建立在Song等人（CVPR 2017）最近的工作基础上，他们提出了SSCnet，这是一种在单个端到端3D卷积网络中执行场景完成和语义标记的方法。SSCnet仅使用深度图作为输入，尽管深度图通常是从也捕获颜色信息的设备（如RGBD传感器和立体相机）中获得的。在这项工作中，我们研究了RGB颜色通道改善SSCnet的潜力。
英文摘要：
Semantic scene completion is the task of producing a complete 3D voxel representation of volumetric occupancy with semantic labels for a scene from a single-view observation. We built upon the recent work of Song et al. (CVPR 2017), who proposed SSCnet, a method that performs scene completion and semantic labelling in a single end-to-end 3D convolutional network. SSCnet uses only depth maps as input, even though depth maps are usually obtained from devices that also capture colour information, such as RGBD sensors and stereo cameras. In this work, we investigate the potential of the RGB colour channels to improve SSCnet.
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。
