Point clouds are one of the emerging 3D visual representations of real word and plenty of useful applications has already been demonstrated. However, a huge amount of data associated with it has added challenges in both transmission and storage. This requires an efficient coding solution and brought a great attention among compression community. MPEG and JPEG standardization group has already started developing coding solution and proposed two test-models namely V-PCC, video-based coding solution, for dynamic point cloud and G-PCC, a native geometry-based coding solution, for static and LiDAR point cloud. In G-PCC, octree (lossless) and tri-soup(lossy) for geometry coding, similarly regional adaptive hierarchical transform (RAHT) and lifting-scheme for attributes coding are currently being explored. Lifting-scheme relies on level-of-details(LOD) structure for attributes prediction where LOD is generated with distance based subsampling approach. In this work we proposed a new LOD generation scheme using binary-tree and showed it provides better coding solution for sparse point cloud such as LiDAR. The experimental results demonstrated 12% bitrate reduction for reflectance and 8%, 6% and 7% bitrate reduction for luma, chroma Cb and chroma Cr respectively as well as up to 4 times computational complexity reduction compared to current G-PCC lifting-scheme.