RESEARCH

COMPUTER VISION

Hierarchical Scene Coordinate Classification and Regression for Visual Localization

June 13, 2020

Abstract

Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The network consists of a series of output layers, each of them conditioned on the previous ones. The final output layer predicts the 3D coordinates and the others produce progressively finer discrete location labels. The proposed method outperforms the baseline regression-only network and allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image RGB localization performance on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and three combined scenes. Moreover, for large-scale outdoor localization on the Aachen Day-Night dataset, we present a hybrid approach which outperforms existing scene coordinate regression methods, and reduces significantly the performance gap w.r.t. explicit feature matching methods.

Download the Paper

AUTHORS

Written by

Xiaotian Li

Shuzhe Wang

Yi Zhao

Jakob Verbeek

Juho Kannala

Publisher

Conference on Computer Vision and Pattern Recognition (CVPR)

Research Topics

Computer Vision

Related Publications

June 15, 2019

COMPUTER VISION

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search | Facebook AI Research

Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive. ConvNet architecture…

Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, Kurt Keutzer

June 15, 2019

April 28, 2019

COMPUTER VISION

Inverse Path Tracing for Joint Material and Lighting Estimation | Facebook AI Research

Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials…

Dejan Azinović, Tzu-Mao Li, Anton Kaplanyan, Matthias Nießner

April 28, 2019

June 14, 2019

COMPUTER VISION

Thinking Outside the Pool: Active Training Image Creation for Relative Attributes | Facebook AI Research

Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is…

Aron Yu, Kristen Grauman

June 14, 2019

September 09, 2018

COMPUTER VISION

DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs | Facebook AI Research

Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest iPhone X. However, they still suffer from heavy noises which dramatically limit their applications. Although plenty of…

Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu

September 09, 2018

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.