论文标题
深隐量压缩
Deep Implicit Volume Compression
论文作者
论文摘要
我们描述了一种新颖的方法，用于压缩3D体素电网中存储的截短签名距离场（TSDF）及其相应的纹理。为了压缩TSDF，我们的方法依赖于基于块的神经网络体系结构端对端训练，从而实现了最先进的利率差异权衡。为了防止拓扑错误，我们无损地压缩了TSDF的符号，TSDF的符号也将重建误差限制在体素大小上。为了压缩相应的纹理，我们设计了一个基于快速块的紫外线参数化，生成相干纹理图，可以使用现有的视频压缩算法有效地压缩。我们证明了在两个4D性能捕获数据集上算法的性能，与最先进的ART相比，相同的失真使比特率降低了66％，或者，对于同一比特量，相同的比特率的失真量降低了50％。
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topological errors, we losslessly compress the signs of the TSDF, which also upper bounds the reconstruction error by the voxel size. To compress the corresponding texture, we designed a fast block-based UV parameterization, generating coherent texture maps that can be effectively compressed using existing video compression algorithms. We demonstrate the performance of our algorithms on two 4D performance capture datasets, reducing bitrate by 66% for the same distortion, or alternatively reducing the distortion by 50% for the same bitrate, compared to the state-of-the-art.
