Monocular Accumulated Depth Estimation with Recursive Feature Fusion

Abstract

Existing depth estimation methods usually use an encoder-decoder structure, but different areas in the image vary in the difficulty and feature demand for depth estimation. This paper proposes a recursive feature fusion monocular depth accumulation estimation method. In the encoder, recursive feature fusion fuses multi-scale features by recursively using gated recurrent units. It extracts features that adapt to the needs of different image areas, replacing cross-layer connections. In the decoder, the depth accumulation estimation decomposes the depth reconstruction process into multiple layers. Different layers predict depth maps of specific granularity and accumulate depth estimation results. Compared with other methods, such as DPT, BTS, and DORN, the proposed method achieved competitive results on two benchmark datasets, KITTI and NYU Depth V2, with Abs Rel reaching 0.058 and 0.107, and RMSE reaching 2.411 and 0.386, respectively.