  Self-supervised multi-frame monocular depth estimation relies on the
geometric consistency between successive frames under the assumption of a
static scene. However, the presence of moving objects in dynamic scenes
introduces inevitable inconsistencies, causing misaligned multi-frame feature
matching and misleading self-supervision during training. In this paper, we
propose a novel framework called ProDepth, which effectively addresses the
mismatch problem caused by dynamic objects using a probabilistic approach. We
initially deduce the uncertainty associated with static scene assumption by
adopting an auxiliary decoder. This decoder analyzes inconsistencies embedded
in the cost volume, inferring the probability of areas being dynamic. We then
directly rectify the erroneous cost volume for dynamic areas through a
Probabilistic Cost Volume Modulation (PCVM) module. Specifically, we derive
probability distributions of depth candidates from both single-frame and
multi-frame cues, modulating the cost volume by adaptively fusing those
distributions based on the inferred uncertainty. Additionally, we present a
self-supervision loss reweighting strategy that not only masks out incorrect
supervision with high uncertainty but also mitigates the risks in remaining
possible dynamic areas in accordance with the probability. Our proposed method
excels over state-of-the-art approaches in all metrics on both Cityscapes and
KITTI datasets, and demonstrates superior generalization ability on the Waymo
Open dataset.
