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Robust three-dimensional scene understanding is now an ever-growing area ofresearch highly relevant in many real-world applications such as autonomousdriving and robotic navigation. In this paper, we propose a multi-tasklearning-based model capable of performing two tasks:- sparse depth completion(i.e. generating complete dense scene depth given a sparse depth image as theinput) and monocular depth estimation (i.e. predicting scene depth from asingle RGB image) via two sub-networks jointly trained end to end using datarandomly sampled from a publicly available corpus of synthetic and real-worldimages. The first sub-network generates a sparse depth image by learning lowerlevel features from the scene and the second predicts a full dense depth imageof the entire scene, leading to a better geometric and contextual understandingof the scene and, as a result, superior performance of the approach. The entiremodel can be used to infer complete scene depth from a single RGB image or thesecond network can be used alone to perform depth completion given a sparsedepth input. Using adversarial training, a robust objective function, a deeparchitecture relying on skip connections and a blend of synthetic andreal-world training data, our approach is capable of producing superior highquality scene depth. Extensive experimental evaluation demonstrates theefficacy of our approach compared to contemporary state-of-the-art techniquesacross both problem domains.

To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation