  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.
