SCE-Depth: A Spherical Compound Eye Framework for Wide FOV Depth Estimation

Accurate depth estimation in wide field is highly desired in applications of autonomous driving, robot vision and drone controls. Biological compound eyes inspire wide Field of View (FOV) depth estimation, yet their artificial implementations face the challenge of modality misalignment. Specifically, the spherical imaging data doesn’t align with the planar neural network, diminishing the learning efficiency. Herein, we propose SCE-Depth, a bio-inspired framework for spherical compound eye depth estimation, which processes spherical images natively on a HEALPix grid using a spherical neural network. This approach achieves a unified $180^\circ$ FOV while avoiding the errors typically introduced by modality conversion. Additionally, we identify a depth-sensitive gradient feature from the overlapping FOVs of adjacent ommatidia. To exploit it, we introduce a spherical Sobel operator called the Spherical Gradient Feature Extractor (SGFE) and a corresponding Spherical Gradient Loss (SGL), which jointly extract gradient features on the HEALPix grid, enabling gradient-aware depth prediction. Extensive benchmark experiments demonstrate that these strategies enable SCE-Depth to substantially reduce depth estimation error compared to fisheye-based baselines, with particularly large improvements in peripheral accuracy. We also demonstrate the generalization capability of SCE-Depth to other wide FOV data modalities, such as fisheye and panoramic imagery.