Recent progress in 3D scene representation, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), has enabled photorealistic rendering with real-time performance. Among these, 3DGS has gained attention due to its capability to represent complex scenes using Gaussian splats. However, this representation introduces significant storage challenges because each splat contains 59 attributes—far more than conventional point clouds. Efficient compression of these attributes, particularly rotation parameters, is essential for practical deployment.

Video-based dynamic mesh compression is being developed for a standard for compressing time-varying mesh data by using existing 2D video codecs. In this paper, we propose a spatial scalable structure for video-based dynamic mesh compression to support scalable applications of the dynamic mesh coding. To support the spatial scalable functionality for geometry, we propose a level of detail based displacement frame packing method that enables decoding up to a certain level. For texture scalable coding, multi-layer texture map is generated from high resolution texture map, and then the scalable high efficiency video coding (SHVC) is employed to remove redundancy between layers. The experimental results demonstrate that our proposed scalable coding method can improve the BD-rate in a 3-layer scalable configuration.

The efficiency of shape coding is an important problem concerning content-based image manipulations and object-based coding of the video sequences. In order to encode the shape information of an object, the boundary is approximated by a polygon which can be encoded with the smallest number of bits for maximum allowable distortion. The conventional boundary coding schemes, however, does not successfully remove the temporal redundancy of the video sequences. This paper proposes a new boundary encoding scheme by which the temporal redundancy between two successive frames is efficiently removed, resulting in lower bit-rate than the conventional algorithms. The interframe vertex selection problem is solved by finding the path with the minimum cost in the directed acyclic graph (DAG) and its fast version using a simplified graph is introduced to reduce the computational load. The vertices were selected from both the current frame to be encoded and the previous frame already encoded, and thus, the temporal redundancy was effectively removed.