Despite considerable progress being achieved in point cloud geometry compression, there still remains a challenge in effectively compressing large-scale scenes with sparse surfaces. Another key challenge lies in reducing decoding latency, a crucial requirement in real-world application. In this paper, we propose Pointsoup, an efficient learning-based geometry codec that attains high-performance and extremely low-decoding-latency simultaneously. Inspired by conventional Trisoup codec, a point model-based strategy is devised to characterize local surfaces. Specifically, skin features are embedded from local windows via an attention-based encoder, and dilated windows are introduced as cross-scale priors to infer the distribution of quantized features in parallel. During decoding, features undergo fast refinement, followed by a folding-based point generator that reconstructs point coordinates with fairly fast speed. Experiments show that Pointsoup achieves state-of-the-art performance on multiple benchmarks with significantly lower decoding complexity, i.e., up to 90$\sim$160$\times$ faster than the G-PCCv23 Trisoup decoder on a comparatively low-end platform (e.g., one RTX 2080Ti). Furthermore, it offers variable-rate control with a single neural model (2.9MB), which is attractive for industrial practitioners.

Pointsoup是一种高性能、极低解码延迟的高效基于学习的几何编解码器，通过将皮肤特征通过基于注意力的编码器嵌入到本地窗口中并引入扩张窗口作为跨尺度先验来推断量化特征的分布，实现了快速的特征细化，然后使用基于折叠的点生成器以相当快的速度重构点坐标。实验证明，Pointsoup在多个基准测试中达到了最先进的性能，且解码复杂度显著降低，即在较低端平台上（例如一块RTX 2080Ti上）与G-PCCv23 Trisoup解码器相比，快速了90-160倍。此外，它还提供变速控制，仅使用一个神经模型（2.9MB），对于工业实践者非常有吸引力。

Pointsoup：用于大规模点云场景的高性能和极低解码延迟的学习几何编解码器