LayoutNetv2:IJCV论文的PyTorch实施:“从单个360图像重构曼哈顿房间的布局:最先进方法的比较”-源码

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LayoutNet v2 我的IJCV论文的PyTorch实施: 从单幅360图像重建曼哈顿房间布局:最先进方法的比较研究 新增:我们的注释数据集已发布! LayoutNet的原始Torch实现在。 LayoutNet的改进 扩展到曼哈顿总体布局(在我们新标记的MatterportLayout数据集上) 使用ResNet编码器而不是SegNet编码器 培训细节和实施细节 基于梯度上升的后期优化,根据Sunset1995的PyTorch修订 添加数据增强 要求 Python 3 PyTorch> = 0.4.0 numpy,scipy,泡菜,skimage,sklearn,随机,cv2,匀称 火炬视觉 Matlab(用于深度渲染) 下载数据和预先训练的模型 下载,并将其放在./model/文件夹下。 下载经过,并将它们放在./data/文件夹下。 从原始下载gt,并在下载已处理的然后将其放在./data/文件夹下 (可选)下载原始LayoutNet的.t7,并将其放在./data/文件夹下 下载我们新标记的,并将其放在./data/文件夹下。 从经过Matterport

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