GroundNet: Monocular Ground Plane Normal Estimation with Geometric Consistency
| Autor: | Xi Li, Yunze Man, Kris M. Kitani, Xinshuo Weng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Monocular Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Correlation 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Segmentation Monocular vision Algorithm Normal 0105 earth and related environmental sciences Ground plane |
| Zdroj: | ACM Multimedia |
| DOI: | 10.48550/arxiv.1811.07222 |
| Popis: | We focus on estimating the 3D orientation of the ground plane from a single image. We formulate the problem as an inter-mingled multi-task prediction problem by jointly optimizing for pixel-wise surface normal direction, ground plane segmentation, and depth estimates. Specifically, our proposed model, GroundNet, first estimates the depth and surface normal in two separate streams, from which two ground plane normals are then computed deterministically. To leverage the geometric correlation between depth and normal, we propose to add a consistency loss on top of the computed ground plane normals. In addition, a ground segmentation stream is used to isolate the ground regions so that we can selectively back-propagate parameter updates through only the ground regions in the image. Our method achieves the top-ranked performance on ground plane normal estimation and horizon line detection on the real-world outdoor datasets of ApolloScape and KITTI, improving the performance of previous art by up to 17.7% relatively. Comment: Camera Ready for ACM MM 2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|