DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts

DoReMi addresses a specific failure mode in 3D Mixture-of-Experts (MoE) architectures: the tendency for routers to focus on semantic content (e.g., 'car') while ignoring topological differences caused by different sensors (e.g., LiDAR density vs. stereo camera depth). While technically sound and addressing a valid niche in robotics and autonomous driving, the project's defensibility is currently low. With 0 stars and 3 forks, it functions primarily as an academic reference implementation for a paper (arXiv:2511.11232v2). The 'moat' here is purely algorithmic; there is no evidence of a large-scale pre-trained model or a proprietary dataset that would prevent a frontier lab (like Waymo, Tesla, or NVIDIA) from implementing similar topology-aware routing into their own perception stacks. Frontier labs are heavily incentivized to build universal 3D foundation models, making this specific architectural tweak a prime candidate for absorption into larger, more generalized systems within 1-2 years. Its value lies in the insight that topology, not just semantics, must drive expert selection in multi-modal 3D environments.