Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point
clouds of an object are surface-aggregated and thus hard to model the
long-range dependency especially for large instances, and that objects are too
close to separate each other. Recent literature addresses these problems by
time-consuming grouping processes such as dual-clustering, mean-shift offsets,
etc., or by bird-eye-view (BEV) dense centroid representation that downplays
geometry. However, the long-range geometry relationship has not been
sufficiently modeled by local feature learning from the above methods. To this
end, we present SCAN, a novel sparse cross-scale attention network to first
align multi-scale sparse features with global voxel-encoded attention to
capture the long-range relationship of instance context, which can boost the
regression accuracy of the over-segmented large objects. For the
surface-aggregated points, SCAN adopts a novel sparse class-agnostic
representation of instance centroids, which can not only maintain the sparsity
of aligned features to solve the under-segmentation on small objects, but also
reduce the computation amount of the network through sparse convolution. Our
method outperforms previous methods by a large margin in the SemanticKITTI
dataset for the challenging 3D PS task, achieving 1st place with a real-time
inference speed.

通过稀疏跨尺度注意力网络，结合全局体素编码注意力捕捉实例上下文的长距离关系，以提高过分割大型对象的回归精度，并采用实例中心的稀疏无类表示来处理面积聚合点，实现稀疏卷积以减少网络计算量。在 SemanticKITTI 数据集上，我们的方法在 3D LiDAR 全景分割任务中取得了很大进展，以实时推理速度获得了第一名。