TCNet: A Temporally Consistent Network for Self-supervised Monocular Depth Estimation

Abstract

Despite significant advances in self-supervised monocular depth estimation methods, achieving temporally consistent and accurate depth maps from frame sequences remains a formidable challenge. Existing approaches often estimate depth maps for individual frames in isolation, neglecting the rich geometric and temporal coherence present across frames. Consequently, this oversight leads to temporally inconsistent outputs, resulting in noticeable temporal flickering artifacts. In response, this paper presents TCNet, a Temporal Consistent Network for self-supervised monocular depth estimation. Specifically, we propose an Inter-frame Temporal Fusion (ITF) module to emphasize the influence of preceding images on the depth estimation of the current frame. The Temporal Consistency Loss (TCL) is proposed to leverage the temporal constraints between the depth maps of adjacent frames. Besides, TCNet can also be applied to both single-frame and multi-frame scenarios during inference. Experimental evaluations on the KITTI dataset demonstrate that our method surpasses state-of-the-art depth estimation methods in accuracy and temporal consistency. Our code will be made public.