Kavli Affiliate: Wei Gao
| First 5 Authors: Chenhao Zhang, Chenhao Zhang, , ,
| Summary:
Dynamic point cloud compression (DPCC) is crucial in applications like
autonomous driving and AR/VR. Current compression methods face challenges with
complexity management and rate control. This paper introduces a novel dynamic
coding framework that supports variable bitrate and computational complexities.
Our approach includes a slimmable framework with multiple coding routes,
allowing for efficient Rate-Distortion-Complexity Optimization (RDCO) within a
single model. To address data sparsity in inter-frame prediction, we propose
the coarse-to-fine motion estimation and compensation module that deconstructs
geometric information while expanding the perceptive field. Additionally, we
propose a precise rate control module that content-adaptively navigates point
cloud frames through various coding routes to meet target bitrates. The
experimental results demonstrate that our approach reduces the average BD-Rate
by 5.81% and improves the BD-PSNR by 0.42 dB compared to the state-of-the-art
method, while keeping the average bitrate error at 0.40%. Moreover, the average
coding time is reduced by up to 44.6% compared to D-DPCC, underscoring its
efficiency in real-time and bitrate-constrained DPCC scenarios. Our code is
available at https://git.openi.org.cn/OpenPointCloud/Ada_DPCC.
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