Depth completion based on multi-scale spatial propagation and tensor decomposition

Abstract

Depth completion aims to generate dense depth maps from sparse depth maps. Existing approaches typically apply a spatial propagation module to iteratively refine depth values based on a single-scale initial depth map. In contrast, to overcome the limitations imposed by the convolution kernel size on propagation range, we propose a multi-scale spatial propagation module (MSSPM) that utilizes multi-scale features from the decoder to guide spatial propagation. To further enhance the model’s performance, we introduce a bottleneck feature enhancement module (BFEM) based on tensor decomposition, which can reduce feature redundancy and perform denoising through low-rank feature factorization. We also introduce a cross-layer feature fusion module (Fusion) to efficiently combine low-level encoder features and high-level decoder features. Extensive experiments on the indoor NYUv2 dataset and the outdoor KITTI dataset demonstrate the effectiveness of the proposed method.