We present Odin, a training system for distributed point-based neural rendering (PBNR) that accelerates training by removing unnecessary global barriers. Odin observes that two training steps need synchronization only when they update overlapping visible parameters. It uses lightweight visibility metadata and a two-phase design with ahead-of-time scheduling plus runtime refinement to handle irregular and dynamic behavior. Odin hides 82% of exposed communication and achieves up to 1.89 × throughput speedup on 64 GPUs while preserving baseline accuracy.