Latent Object Embedding for Self-Supervised Monocular Depth Estimation

Abstract

Extracting 3D information from 2D images is highly significant, and self-supervised monocular depth estimation has demonstrated great potential in this field. However, existing methods primarily focus on estimating depth from immediate visual features, leading to severe foreground-background adhesion, which poses challenges for achieving precise depth estimation. In this paper, we propose a depth estimation method called LOEDepth, which can implicitly distinguish foreground objects from the background. In LOEDepth, a latent object embedding module is introduced, which leverages a set of learnable queries to generate latent object proposals from both immediate visual features extracted by the encoder and sparse object features derived through multi-scale deformable attention. These latent object proposals are utilized to perform soft classification on the decoded features to distinguish foreground objects from the background. Additionally, as depth boundaries do not always align with semantic boundaries, we propose a novel deep decoder to provide decoding features with rich spatial location retrieval and semantic information. Finally, two mask strategies are utilized to conceal pixels violating the scene's static assumption, so as to mitigate disruptions caused by abnormal pixels during self-supervised training. Experimental results on the KITTI and Make3D datasets demonstrate significant performance improvements and robust fine-grained scene depth estimation capabilities of the proposed method.