APMVS: Learning Multi-View Stereo Based on Adjacent Stage and Pair-Wise Stage Uncertainty Estimation

Many multi-view stereo (MVS) networks with a cascaded structure can effectively estimate depth while saving memory. However, the accuracy of the depth map in the fine stage depends on the depth map estimated in the coarse stage. Additionally, the multi-stage depth maps generated by the cascaded structure are used to compute losses but are not reused, resulting in a loss of inter-stage differentiation information. To address these issues, we propose a dual-uncertainty estimation MVS method that learns an MVS network based on adjacent stage and pair-wise stage uncertainty estimation, named APMVS. The core of the proposed APMVS is to employ dual-uncertainty estimation to mitigate the adverse effects of the cascaded structure. Specifically, it involves two estimation modules: adjacent stage uncertainty (ASU) and pair-wise stage uncertainty (PSU). The ASU estimation module dynamically adjusts the depth-hypothesis range by leveraging uncertainty from the previous stage, thereby improving the accuracy of depth-map prediction in the current stage. The PSU estimation module estimates the uncertainty between each pair of stages. Thus, regions with high uncertainty have minimal impact. We evaluate the proposed APMVS on the DTU, Tanks and Temples, and BlendedMVS datasets. Experimental results show that our method achieves superior reconstruction quality compared with other state-of-the-art methods.