  The sparse LiDAR point clouds become more and more popular in various
applications, e.g., the autonomous driving. However, for this type of data,
there exists much under-explored space in the corresponding compression
framework proposed by MPEG, i.e., geometry-based point cloud compression
(G-PCC). In G-PCC, only the distance-based similarity is considered in the
intra prediction for the attribute compression. In this paper, we propose a
normal-based intra prediction scheme, which provides a more efficient lossless
attribute compression by introducing the normals of point clouds. The angle
between normals is used to further explore accurate local similarity, which
optimizes the selection of predictors. We implement our method into the G-PCC
reference software. Experimental results over LiDAR acquired datasets
demonstrate that our proposed method is able to deliver better compression
performance than the G-PCC anchor, with $2.1\%$ gains on average for lossless
attribute coding.
