论文笔记——Semantic Scene Completion from a Signal Depth Image

xiaoxiao2021-02-28  4

没有太多时间好好翻译了,大概把要点梳理了一下啦~


Task:

produce a complete 3D voxel representation of volumetric occupancysemantic labels for a scene from a signal-view depth map observation

以前的类似工作呢一直是将1、2任务分开完成的,但作者设计了一个end-to-end 3D Conv net:SSCNet用于完善语义场景,发现效果更好。 input:a single depth image output:occupancy and semantic labels

如何解决Semantic Scene Completion的思路:

A dilation-based 3D context moduleJoint model

Problems and Solution:

How do we effective capture contextual information from 3D volumetric data,where the signal is sparse and lacks high frequency detail? *Solution:*A dilation-based 3D context module用于扩大感受野,能够有效捕捉来自三维体积数据的信息,其中信号是稀疏且缺乏高频细节

RGB-D datasets only provide annotationos on visible surfaces,how do we obtain training data with complete volumetric annatations at scene level? *Solution:*SUNCG datasts(we can compute 3D secen volumes with dense object labels through voxelization) (RGB-D仅仅提供了浅层表面的注释)

Attribute:

提出SSCNet,该网络的输入是单幅的深度图像,输出包括了体素和语义标签。提出了一个新的数据集SUNCG,该数据集是dense注释的合成三维场景数据集。

相关的算法都是建立在相机模型的基础之上,分单目(即单视角)和双目(即多视角,multi-view),而本论文是single-view。

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