GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory
GEM-Occ creates a structured spatial memory for robots by merging visual evidence into persistent semantic Gaussian occupancy maps. It uses a framework to convert transient visual predictions into long-term memory, allowing agents to build and update detailed indoor maps through causal fusion of local geometry and semantic information.