Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps

Abstract

This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions. In a supervised learning scenario, the quality of predictions is intrinsically related to the training labels, which guide the optimization process. For indoor scenes, structured-light-based depth sensors (e.g. Kinect) are able to provide dense, albeit short-range, depth maps. On the other hand, for outdoor scenes, LiDARs are considered the standard sensor, which comparatively provides much sparser measurements, especially in areas further away. Rather than modifying the neural network architecture to deal with sparse depth maps, this article introduces a novel densification method for depth maps, using the Hilbert Maps framework. A continuous occupancy map is produced based on 3D points from LiDAR scans, and the resulting reconstructed surface is projected into a 2D depth map with arbitrary resolution. Experiments conducted with various subsets of the KITTI dataset show a significant improvement produced by the proposed Sparse-to-Continuous technique, without the introduction of extra information into the training stage.

Accepted. (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• TensorFlow: A system for large-scale machine learning
• A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• Computing the Stereo Matching Cost with a Convolutional Neural Network
• From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
• J-MOD${}^2$: Joint Monocular Obstacle Detection and Depth Estimation
• The BerHu penalty and the grouped effect
• Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors

Cited by in corpus (2)

• On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation
• Towards Real-Time Monocular Depth Estimation for Robotics: A Survey