A Sparsity-Invariant Model via Unifying Depth Prediction and Completion

Abstract

The development of a sparse-invariant depth completion model capable of handling varying levels of input depth sparsity is highly desirable in real-world applications. However, existing sparse-invariant models tend to degrade when the input depth points are extremely sparse. In this paper, we propose a new model that combines the advantageous designs of depth completion and monocular depth estimation tasks to achieve sparse invariance. Specifically, we construct a dual-branch architecture with one branch dedicated to depth prediction and the other to depth completion. Additionally, we integrate the multi-scale local planar module in the decoders of both branches. Experimental results on the NYU Depth V2 benchmark and the OPPO prototype dataset equipped with the Spot-iToF316 sensor demonstrate that our model achieves reliable results even in cases with irregularly distributed, limited or absent depth information.