BIRD-PCC: 双方向レンジ画像ベースのLiDAR点群深層圧縮
BIRD-PCC: Bi-directional Range Image-based Deep LiDAR Point Cloud Compression
LiDAR点群圧縮のため、無効画素を考慮した双方向予測と深層学習による残差符号化を組み合わせたフレームワークを提案し、既存手法を上回る性能を示した。
著者: Chia-Sheng Liu, Jia-Fong Yeh, Hao Hsu, Hung-Ting Su, Ming-Sui Lee, Winston H. Hsu
分類: cs.MM, cs.RO
原文アブストラクト
The large amount of data collected by LiDAR sensors brings the issue of LiDAR point cloud compression (PCC). Previous works on LiDAR PCC have used range image representations and followed the predictive coding paradigm to create a basic prototype of a coding framework. However, their prediction methods give an inaccurate result due to the negligence of invalid pixels in range images and the omission of future frames in the time step. Moreover, their handcrafted design of residual coding methods could not fully exploit spatial redundancy. To remedy this, we propose a coding framework BIRD-PCC. Our prediction module is aware of the coordinates of invalid pixels in range images and takes a bidirectional scheme. Also, we introduce a deep-learned residual coding module that can further exploit spatial redundancy within a residual frame. Experiments conducted on SemanticKITTI and KITTI-360 datasets show that BIRD-PCC outperforms other methods in most bitrate conditions and generalizes well to unseen environments.
