Hybrid Explicit–Implicit Dense Mapping with Quality-Guided Refinement and Residual Feedback

Abstract. Real-time dense SLAM must balance geometric fidelity and computational efficiency for autonomous navigation. Explicit mapping methods provide stable global structure and fast updates, but suffer from discretization artifacts and memory overhead. Implicit neural representations capture continuous surfaces and fine details, yet require expensive optimization and are sensitive to initialization. Existing hybrid approaches combine both paradigms, but often allocate neural refinement inefficiently and remain vulnerable to pose errors. To address these limitations, we propose a selective hybrid dense mapping framework that couples a scene-wide TSDF backbone with quality-guided implicit local refinement and residual-guided sliding-window pose feedback. Neural refinement is activated only in low-quality regions identified by multi-indicator assessment, while keyframe poses are re-optimized using residuals from explicit raycasting and implicit rendering. Experiments on TUM RGB-D and Replica demonstrate improved mapping accuracy, localization robustness, and real-time efficiency.