MIX3D: A Mixed Representation for Communication-Efficient Distributed 3DGS Training

3D Gaussian Splatting (3DGS) has recently emerged as a prominent technique in novel view synthesis. The superior performance of 3DGS has catalyzed an increasing number of 3DGS- based applications in edge scenarios, where 3DGS is utilized for various purposes, such as scene representation, comprehension, and generation. Meanwhile, these edge applications also serve as primary sources of scene observations for producing 3DGS models. However, the intensive computation involved in 3DGS training and the massive number of 3D Gaussian primitives required for high-resolution scene repre-sentation hinder the effectiveness of in-situ 3DGS training on off-the-shelf edge devices, whether using standalone training or Data-Distributed-Parallel (DDP) training. To address this issue, this work proposes MIX3D, a novel mixed representation for communication-efficient distributed 3DGS training in edge scenarios. MIX3D features a global sparse sub-model and various local dense sub-models, where the sparse sub-model encodes coarse-grained appearance for the entire scene, and each dense sub-model targets fine-grained details for a specific region of the scene. Extensive evaluations on a four-device edge cluster demonstrate the effectiveness of our developed distributed 3DGS training workflow based on MIX3D, achieving reductions in training time up to 86.6% compared to vanilla DDP training and an average speedup of 3.767x over standalone training.