Accurate camera localization is an essential part of tracking systems. However, localization results are greatly affected by illumination. Including data collected under various lighting conditions can improve the robustness of the localization algorithm to lighting variation. However, this is very tedious and time consuming. By using synthesized images it is possible to easily accumulate a large variety of views under varying illumination and weather conditions. Despite continuously improving processing power and rendering algorithms, synthesized images do not perfectly match real images of the same scene, i.e. there exists a gap between real and synthesized images that also affects the accuracy of camera localization. To reduce the impact of this gap, we introduce "REal-to-Synthetic Transform (REST)." REST is an autoencoder-like network that converts real features to their synthetic counterpart. The converted features can then be matched against the accumulated database for robust camera localization. In our experiments REST improved feature matching accuracy under variable lighting conditions by approximately 30 . Moreover, our system outperforms state of the art CNN-based camera localization methods trained with synthetic images. We believe our method could be used to initialize local tracking and to simplify data accumulation for lighting robust localization.
[id=AP] Feature databases can be obtained through Structure-from-Motion reconstruction @cite_33 , Simultaneous Localization and Mapping methods @cite_34 @cite_11 @cite_31 @cite_8 @cite_12 @cite_9 , geo-annotated image datasets @cite_10 , or 3D scans of the environment @cite_24 @cite_23 . As all of these methods reconstruct the scene appearance at a particular point in time, they are not robust to drastic changes in lighting conditions. To account for these changes, the database must be generated multiple times under different conditions.
@cite_33 @cite_8 @cite_9 @cite_24 @cite_23 @cite_31 @cite_34 @cite_10 @cite_12 @cite_11
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In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50 ) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results. This paper presents a simple and robust method, namely discriminative histograms of local dominant orientation (D-HLDO), for biometric image feature extraction. In D-HLDO, the local dominant orientation map and the corresponding relative energy map are obtained by applying the singular value decomposition (SVD) to the collected gradient vectors over a local patch. The dominant orientation map and the relative energy map are then used to construct the concatenated histogram features. Local mean based nearest neighbor discriminant analysis (LM-NNDA) is finally employed to reduce the redundancy information and get the low-dimensional and discriminative features. The proposed method is applied to face, finger-knuckle-print and Palm biometrics and is examined using the AR, CMU PIE and FRGCv2.0 face image databases, the PolyU Palmprint database, and the PolyU Finger-Knuckle-Print database. Experimental results demonstrate the effectiveness of the proposed D-HLDO method. Localizing the user from a feature database of a scene is a basic and necessary step for presentation of localized augmented reality (AR) content. Commonly such a database depicts a single appearance of the scene, due to time and effort required to prepare it. However, the appearance depends on various factors, e.g., the position of the sun and cloudiness. Observing the scene under different lighting conditions results in a decreased success rate and accuracy of the localization. To address this we propose to generate the feature database from a simulated appearance of the scene model under a number of different lighting conditions. We also propose to extend the feature descriptors used in the localization with a parametric representation of their changes under varying lighting conditions. We compare our method with a standard representation and matching based on L2-norm in a simulation and real world experiments. Our results show that our simulated environment is a satisfactory representation of the scene's appearance and improves feature matching over a single database. The proposed feature descriptor achieves a higher localization ratio with fewer feature points and a lower process cost. We present a fast, simple location recognition and image localization method that leverages feature correspondence and geometry estimated from large Internet photo collections. Such recovered structure contains a significant amount of useful information about images and image features that is not available when considering images in isolation. For instance, we can predict which views will be the most common, which feature points in a scene are most reliable, and which features in the scene tend to co-occur in the same image. Based on this information, we devise an adaptive, prioritized algorithm for matching a representative set of SIFT features covering a large scene to a query image for efficient localization. Our approach is based on considering features in the scene database, and matching them to query image features, as opposed to more conventional methods that match image features to visual words or database features. We find this approach results in improved performance, due to the richer knowledge of characteristics of the database features compared to query image features. We present experiments on two large city-scale photo collections, showing that our algorithm compares favorably to image retrieval-style approaches to location recognition.