A Single Encoder-Dual Decoder Framework For End-To-End Learned Point Cloud Compression
A Single Encoder-Dual Decoder Framework For End-To-End Learned Point Cloud Compression
National Chung Cheng University, TaiwanNational Chung Cheng University, Taiwan
Fig. 1 Existing Learning-based point cloud compression solutions, (a) Geometry compression. (b) Attribute compression.
(c) Joint geometry and attribute compression through recoloring.
(d) Proposed SEDD-PCC: an end-to-end learned point cloud compression scheme.
圖 1 現有基於深度學習之點雲壓縮方法,(a) 幾何壓縮。(b) 屬性壓縮。
(c) 透過重新上色的聯合幾何和屬性壓縮。
(d) 提出的 SEDD-PCC:聯合幾何與屬性之點雲壓縮方法。
🔍 Problem: Most learning-based compression schemes treat geometry and attribute coding separately, employing distinct encoders and decoders. This not only increases computational complexity but also fails to fully exploit shared features between geometry and attributes.
🔍 問題: 大多數基於深度學習的壓縮方法將幾何和屬性分開處理,使用不同的編碼器和解碼器。這不僅增加了計算複雜性,而且無法充分利用幾何和屬性之間的共享特徵。
✅ Our Solution: To address this limitation, we propose SEDD-PCC, an end-to-end learning-based framework for lossy point cloud compression that jointly compresses geometry and attributes. SEDD-PCC employs a single encoder to extract shared geometric and attribute features into a unified latent space, followed by dual specialized decoders that sequentially reconstruct geometry and attributes.
✅ 我們的解決方法: 為了解決這一限制,我們提出了 SEDD-PCC,這是一個端到端的基於深度學習的有損點雲壓縮架構,可以聯合壓縮幾何和屬性。SEDD-PCC 採用單一編碼器將共享的幾何和屬性特徵提取到統一的潛在空間中,然後透過兩個量身打造解碼器首先重建幾何資訊再將其資訊傳遞給屬性解碼器來完成整個重建過程。
🎯 Enhancement: We incorporate knowledge distillation to enhance feature representation learning from a teacher model, further improving coding efficiency. With its simple yet effective design, SEDD-PCC provides an efficient and practical solution for point cloud compression.
🎯 增強: 我們結合知識蒸餾來增強從教師模型中學習特徵,進一步提高編碼效率。憑藉其簡單而有效的設計,SEDD-PCC為點雲壓縮提供了高效且實用的解決方法。
📊 Results: Comparative evaluations against both rule-based and learning-based methods demonstrate its competitive performance, highlighting SEDD-PCC as a promising AI-driven compression approach.
📊 結果: 與傳統的MPEG標準相比基於深度學習的點雲壓縮方法證明了其具有競爭力的性能,凸顯了SEDD-PCC作為一種有前途的 AI 驅動壓縮方法。
Figure 2: The three-stage training strategy.
圖 2:三階段訓練策略。
Figure 3: The proposed SEDD-PCC architecture.
圖 3:提出的 SEDD-PCC 架構。
Fig. 4 R-D performance of the proposed scheme in terms of 1-PCQM.
圖 4 根據 1-PCQM 指標提出方法的 R-D 性能。
Table 1. BD-rate (%) against G-PCCv23 for various schemes.
表 1. 各種方法對比 G-PCCv23 的 BD-rate (%)。