In video-based point cloud compression (V-PCC), the partitioning of the Coding Unit (CU) has ultra-high computational complexity. Just Noticeable Difference Model (JND) is an effective metric to guide this process. However, in this paper, it is found that the performance of traditional JND model is degraded in V-PCC. For the attribute video, due to the pixel-filling operation, the capability of brightness perception is reduced for the JND model. For the geometric video, due to the depth filling operation, the capability of depth perception is degraded in the boundary area for depth based JND models (JNDD). In this paper, a joint JND model (J_JND) is proposed for the attribute video to improve the brightness perception capacity, and an occupancy map guided JNDD model (O_JNDD) is proposed for the geometric video to improve the depth difference estimation accuracy of the boundaries. Based on the two improved JND models, a fast V-PCC Coding Unit (CU) partitioning algorithm is proposed with adaptive CU depth prediction. The experimental results show that the proposed algorithm eliminates 27.46% of total coding time at the cost of only 0.36% and 0.75% Bjontegaard Delta rate increment under the geometry Point-to-Point (D1) error and attribute Luma Peak-signal-Noise-Ratio (PSNR), respectively.