3D Gaussian splatting enables real-time, photo-realistic scene rendering but is challenged by redundant, high-dimensional primitive data. To make immersive AR/VR and volumetric communication practical, compression must address both temporal and attribute redundancy. We introduce a streamlined inter-prediction coding pipeline that predicts each P-frame's color and geometry attributes from a nearby reference using a lightweight hybrid predictor, combining bilateral filtering and K-nearest-neighbor feature transfer, and encodes only residuals. Our approach targets the most rate-critical channels by applying codebook-based vector quantization to spherical harmonic components, while retaining efficient scalar quantization for other attributes. All quantized streams and codebooks are entropy coded. The proposed method achieves an average 89.1 % reduction in BD-rate and a 12.39 dB increase in BD-PSNR over GPCCv1, and outperforms previous SOTA interGS by 18.6 % in BD-rate and 1.81 dB in BD-PSNR. With practical decoding speeds (0.605 seconds per frame), interGS-Lite preserves real-time rendering and has potential for streaming and AR/VR applications, delivering consistent bitrate reductions with high visual quality.

A point cloud is a 3D data representation that presents unique challenges due to its large volume, high dimensionality, and lack of structure. This work introduces FDIP-PCAC, a$F$requency$D$omain$I$nter$P$rediction and Compensation system for dynamic point cloud attribute compression. The method combines deep learning, geometry-driven modeling, and Graph Fourier Transform (GFT) within a five-phase encoder–decoder framework. Initially, the reference and target point cloud frames are partitioned into corresponding nodes using a binary tree. In Phase 1, each target node identifies its K-nearest reference nodes based on the geometry centroid. Phase 2 computes the GFT latent representation of these selected reference nodes. Phase 3 and 4 transfer the RGB colors from matched reference nodes to target nodes, generating Predicted Target Node and Second-Stage Predicted Target Node, respectively. In phase 5, the neural network-based FreqNeRF predicts the GFT latent representation of target nodes. The proposed system achieves superior compression and reconstruction performance compared to G-PCC across diverse datasets. It delivers average BD-rate reductions of 65.89% against RAHT-v30, 63.61% against PredLift-v30, 67.59% against RAHT-v23 and 66.82% against PredLift-v23.