论文标题
具有密度的深点云压缩
Density-preserving Deep Point Cloud Compression
论文作者
论文摘要
点云的局部密度对于表示本地细节至关重要，但是现有点云压缩方法已忽略了。为了解决这个问题，我们提出了一种新型的深点云压缩方法，以保留局部密度信息。我们的方法以自动编码方式起作用：编码器下调了点并了解点的特征，而解码器则使用这些功能为点示例。具体而言，我们建议用三个嵌入来编码局部几何和密度：密度嵌入，局部位置嵌入和祖先嵌入。在解码过程中，我们明确预测了每个点的上采样因子以及UPS采样点的方向和尺度。为了减轻现有方法中的群集点问题，我们设计了一个新型的子点卷积层，并具有自适应量表的提升块。此外，我们的方法还可以压缩诸如正常的角度属性。 Semantickitti和Shapenet的广泛的定性和定量结果表明，我们的方法实现了最先进的利率差异权衡。
Local density of point clouds is crucial for representing local details, but has been overlooked by existing point cloud compression methods. To address this, we propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsamples the points and learns point-wise features, while the decoder upsamples the points using these features. Specifically, we propose to encode local geometry and density with three embeddings: density embedding, local position embedding and ancestor embedding. During the decoding, we explicitly predict the upsampling factor for each point, and the directions and scales of the upsampled points. To mitigate the clustered points issue in existing methods, we design a novel sub-point convolution layer, and an upsampling block with adaptive scale. Furthermore, our method can also compress point-wise attributes, such as normal. Extensive qualitative and quantitative results on SemanticKITTI and ShapeNet demonstrate that our method achieves the state-of-the-art rate-distortion trade-off.
