CR-FusionNet: Hybrid Stereo-Monocular Fusion for Depth Perception in Autonomous and Robotic Systems

Stereo matching yields metrically grounded disparity but often fails in textureless, repetitive, reflective, or occluded regions, while monocular depth estimation provides dense structure yet suffers from scale ambiguity and weaker local geometric fidelity. We present CR-FusionNet, a lightweight stereo– monocular fusion framework designed for resource-constrained robotic perception. Our pipeline uses CPU-based Semi-Global Block Matching (SGBM) as a deterministic metric anchor and fuses it with a monocular prior through a compact fully convolutional encoder–decoder. Before fusion, we align the monocular prediction to the stereo disparity scale via robust affine regression on valid stereo pixels. CR-FusionNet then performs confidence-aware routing between stereo and monocular cues and applies residual refinement to mitigate SGBM discretization artifacts, producing dense and stable metric depth. Experiments on KITTI Stereo 2015, Middlebury, and TartanAir show consistent improvements over single-source baselines while requiring significantly less GPU memory than deep stereo networks, supporting efficient onboard deployment.