阅读报告——LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY COMPRESSION Worth noticing in Introduction 质量评价衡量: D1 and D2 D1:计算重建点和K近邻之间的MSE D2:计算重建点和超平面之间的MSE Main Idea 对于基于传统八叉树模型
Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds https://arxiv.org/abs/1905.03691 Sampling layer 在G-PCC中,基于八叉树的几何编码根据量化尺度来控制有损几何压缩,设输入点云为: G-PCC编码器的量化计算如下: 其中 X s h i f t X
在图像预处理/图片处理的过程中,会带有把uint8图像转换成float64类型的警告提示:Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning. 这时只要添加一行代码即可: dst = (dst*255.0).as
High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation 文章目录 High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information AggregationAbstratI. In
文章目录 IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression采样点的octree编码集成的压缩和训练过程熵编码的可学习上下文模型对抗学习均匀性度量实验结果 IPDAE: Improved Patch-Based Deep Autoencoder for Lo