FLaTEC：频率解耦的潜在三平面用于激光雷达点云高效压缩
FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds
AI总结：
FLaTEC通过频率解耦的潜在三平面方法，实现激光雷达点云的高效压缩，达到最佳率失真性能，并在多个数据集上超越传统编码器。
摘要：
点云压缩方法联合优化比特率和重建失真。然而，平衡压缩比和重建质量具有挑战性，因为低频和高频成分在相同分辨率下贡献不同。为此，我们提出了FLaTEC，一种频率感知的压缩模型，能够高效压缩完整的扫描数据。我们的方法引入了一种频率感知机制，将低频结构和高频纹理解耦，同时利用潜在三平面作为紧凑的点云代理。具体来说，我们将体素化嵌入转换为三平面表示，以减少稀疏性、计算成本和存储需求。然后，我们设计了一种频率解耦技术，提取紧凑的低频内容并收集不同尺度的高频细节。解耦的低频和高频组件以二进制格式存储。在解码过程中，通过调制块逐步恢复全频谱信号。此外，为了补偿3D相关性的损失，我们引入了一种高效的基于频率的注意力机制，促进局部连接并输出任意分辨率的点。我们的方法在率失真性能上达到最先进的水平，并在SemanticKITTI和Ford数据集上分别比标准编码器提高了78%和94%的BD-rate。
英文摘要：
Point cloud compression methods jointly optimize bitrates and reconstruction distortion. However, balancing compression ratio and reconstruction quality is difficult because low-frequency and high-frequency components contribute differently at the same resolution. To address this, we propose FLaTEC, a frequency-aware compression model that enables the compression of a full scan with high compression ratios. Our approach introduces a frequency-aware mechanism that decouples low-frequency structures and high-frequency textures, while hybridizing latent triplanes as a compact proxy for point cloud. Specifically, we convert voxelized embeddings into triplane representations to reduce sparsity, computational cost, and storage requirements. We then devise a frequency-disentangling technique that extracts compact low-frequency content while collecting high-frequency details across scales. The decoupled low-frequency and high-frequency components are stored in binary format. During decoding, full-spectrum signals are progressively recovered via a modulation block. Additionally, to compensate for the loss of 3D correlation, we introduce an efficient frequency-based attention mechanism that fosters local connectivity and outputs arbitrary resolution points. Our method achieves state-of-the-art rate-distortion performance and outperforms the standard codecs by 78\% and 94\% in BD-rate on both SemanticKITTI and Ford datasets.
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