Multi-scale Depth-Calibrated Kernel-split Network for Monocular Occupancy Prediction

In the task of indoor occupancy prediction from a single im- age, there is an issue where it is challenging to complement the semantic scene due to the small size and intricate nature of the predicted objects. Traditional methods struggle to obtain rich and accurate semantic infor- mation, which results in the worsening of the problem of semantic scene blurring. To solve the problem, we propose a novel approach accompa- nied by our proposed Multi-scale Context Calibration Module MCCM , Depth Calibrated Residual Network DCRNet and Kernel-split Depth- wise Attention KSDA to enhance the scene semantic information and alleviate the depth blurring problem of semantic scenes. Ablation ex- periments confirm the effectiveness of our module. Comparative analysis with the SOTA model verifies the superiority and generalisation of our model. Comparison on the OccScanNet_mini dataset confirms the ex- cellent generalisation of our method even with limited data. Specifically, our method reaches 47.94 and 32.33 for IoU and mIoU on the NYUv2 dataset, and 42.81 and 29.59 for IoU and mIoU on the OccScanNet dataset, respectively.