光達傳感器所收集的大量資料也隨之產生了光達資料壓縮的問題。而基於距離影像的光達資料壓縮方法是一個有潛力的候選解決方案,但是在幀間編碼方面缺乏充分的探索。在我們的工作中,我們嘗試處理基於距離影像的光達傳感器資料幀間壓縮問題。我們的幀間壓縮框架遵循預測編碼的典範,該典範已在彩色影像編碼領域被採用。我們提出的框架包括一個預測模組和一個殘差編碼模組。對於預測模組,我們將時間序列中的前一幀和後一幀作為參考幀,並對將要編碼的幀進行幀級預測。至於殘差編碼模組,與以往先量化再進行無損編碼的方法不同,我們引入了一種基於學習的方法,可以進一步開發殘差幀內的空間冗餘性。在SemanticKITTI資料集上進行的實驗顯示出,在低碼率的條件之下,我們的方法優於其他基於距離影像的方法。
The large amount of data collected by LiDAR sensors brings the issue of LiDAR data compression. Range image-based method for LiDAR data compression is a potential candidate for solution but lacks full exploration, especially for inter-frame coding. In our work, we address the problem of range image-based LiDAR sensor data inter-frame compression. Our inter-frame compression framework follows the predictive coding paradigm, which is adopted in the field of color video codec. Our proposed framework consists of a prediction module and a residual coding module. For the prediction module, we take the previous and next frame in time steps as reference frames, aiming to perform a frame-level prediction of the to-be-encoded frame. As for the residual coding module, different from previous methods that first apply quantization followed by a lossless coder, we introduce a learning-based approach that can further exploit spatial redundancy within the residual frame. Experiments conducted on the SemanticKITTI dataset demonstrate that our method outperforms other range image-based methods, especially at low bitrate conditions.