LaserMix框架概览。(a) LiDAR点云包含着很强的结构先验;环绕于自车周围的物体和背景在不同的laser beam(如上、中、下)上有着不同的分布表现。(b) 基于LaserMix的框架适用于各种流行的点云表征,如range view和voxel。(c) LaserMix在low-data和high-data条件下取得了超过各种SoTA方法的表现 基于上述发现,这个工...
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从表中结果可以看出,LaserMix极大地提升了半监督条件下的LiDAR分割结果。无论是在不同的数据集还是不同的LiDAR点云表征下,LaserMix的分割结果都明显地超过了Sup.-only和SoTA的半监督学习方法。 与当前的SoTA点云半监督学习方法GPC 对比(如下表左所示),LaserMix亦在多种半监督数据切分条件下取得了明显优异的性能,...
至此,我们可以正式引出LaserMix,本文中所提出的半监督LiDAR点云分割框架,其所提出的划分策略可以很好地控制结构先验(spatial prior)的“强度”。具体地,LaserMix将前文提到的区域集按照交织(intertwine)的形式进行混合,即:其中之一由奇数项区域混合而得,...
Figure 4.Framework overview. Labeled scan is fed into the Student net to compute the supervised loss (w/ ground-truth). Unlabeled scan and the generated pseudo-label are mixed with the labeled scan and its labels via LaserMix to produce mixed data, which is then fed into the Student net ...
Laser mix-and-matchLaserVascular LesionsSkin RejuvenationThe article focuses on a presentation by International Master Course on Aging Skin that suggests that some newer-generation laser models, which may be used in conjunction ...
LaserMix for Semi-Supervised LiDAR Semantic Segmentation Lingdong Kong,Jiawei Ren,Liang Pan,Ziwei Liu S-Lab, Nanyang Technological University About LaserMixis a semi-supervised learning (SSL) framework designed for LiDAR semantic segmentation. It leverages the strongspatial priorof driving scenes to co...
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