Propagating Confidences through CNNs for Sparse Data Regression

Abstract

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.

arXiv commentTo appear in the British Machine Vision Conference (BMVC2018)

Cited by in corpus (8)

• Confidence Propagation through CNNs for Guided Sparse Depth Regression
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• DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image
• CU-Net: LiDAR Depth-Only Completion With Coupled U-Net
• Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction
• 3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image
• Unsupervised Depth Completion from Visual Inertial Odometry
• Depth Edge Guided CNNs for Sparse Depth Upsampling