GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory

2026-07-06

AI Summary

This work addresses the challenge of achieving long-term consistent semantic occupancy mapping across interconnected indoor spaces. To this end, we propose GEM-Occ, a novel framework that introduces a Gaussian evidence memory–based mechanism for semantic occupancy mapping. Our approach converts local visual predictions into semantic Gaussian occupancy and free-space ray evidence, which are then integrated into a hierarchical persistent memory structure—comprising local buffers, room-level subgraphs, and building-scale graphs—via a visibility- and uncertainty-aware causal update strategy. The framework further enables efficient Gaussian-to-occupancy splatting for rapid querying. Evaluated on the HIOcc benchmark, GEM-Occ substantially outperforms existing methods in local accuracy, online stability, free-space reasoning, revisit consistency, and building-scale scalability.

Abstract

Semantic occupancy provides a structured spatial memory for embodied indoor agents by jointly representing occupied regions, observed free space, unknown areas, and object semantics. However, existing indoor occupancy benchmarks and methods mainly focus on single-view prediction or room-level online perception, leaving long-horizon semantic mapping across connected indoor spaces underexplored. We introduce HIOcc, a hierarchical indoor occupancy benchmark that unifies ScanNet, ScanNet++, and Matterport3D under a common sparse semantic occupancy format while preserving their native observation geometries, including perspective RGB-D frames and pano-centric observation groups. HIOcc supports three complementary evaluation regimes: local semantic occupancy prediction, room-level online occupancy mapping, and building-level mapping across connected panoramic environments. We further propose GEM-Occ, a Gaussian Evidence Memory framework for semantic occupancy mapping. Rather than using pointmaps as persistent map states, GEM-Occ treats local visual geometry predictions as transient evidence, converts them into semantic Gaussian occupancy evidence and free-space ray evidence, and fuses them into a persistent hierarchical memory through visibility- and uncertainty-aware causal updates. The memory is organized into local caches, room-level submaps, and a building-level graph, and can be queried at any time through Gaussian-to-occupancy splatting. Experiments on HIOcc show that GEM-Occ improves local occupancy prediction, online map stability, free-space reasoning, revisit consistency, and building-level scalability over prior indoor occupancy and Gaussian-based mapping baselines.

Problem

• semantic occupancy
• indoor mapping
• long-horizon perception
• embodied agents
• spatial memory

Innovation

• semantic occupancy
• Gaussian evidence memory
• hierarchical mapping
• indoor spatial memory
• visibility-aware fusion