MemOcc: Hierarchical Memory for Indoor Continuous Occupancy Mapping

Abstract

Indoor 3D occupancy mapping, crucial for robotic perception, struggles with occlusions and reappearing surfaces in continuous observations. Existing methods either fuse frames without discernment, causing occlusion-induced errors to persist and contaminate global representations, or recompute scenes from scratch, sacrificing efficiency and stability. To address these challenges, we propose MemOcc, a novel memory-augmented framework for continuous occupancy mapping using read–write–retrieve operations. MemOcc employs a hierarchical memory design with cooperative short- and long-term tiers. Its Short-Term Memory Cache module uses visibility-gated writes and confidence maps to stabilize voxel predictions and filter occlusion noise, while the Long-Term Memory Bank stores scene priors for rapid retrieval, accelerating convergence in revisited regions. As a plug-and-play module, MemOcc integrates seamlessly with existing 2D-to-3D pipelines without altering backbones or training. Experiments on indoor benchmarks demonstrate MemOcc reduces error propagation by 25% and improves mapping speed over state-of-the-art methods, achieving robust, real-time performance. By selectively retaining reliable evidence and enabling efficient retrieval, MemOcc paves the way for scalable indoor perception in robotics and augmented reality.