Camera Pose Voting for Large-Scale Image-Based Localization

Abstract

Image-based localization approaches aim to determine the camera pose from which an image was taken. Finding correct 2D-3D correspondences between query image features and 3D points in the scene model becomes harder as the size of the model increases. Current state-of-theart methods therefore combine elaborate matching schemes with camera pose estimation techniques that are able to handle large fractions of wrong matches. In this work we study the benefits and limitations of spatial verification compared to appearance-based filtering. We propose a voting-based pose estimation strategy that exhibits O(n) complexity in the number of matches and thus facilitates to consider much more matches than previous approaches -whose complexity grows at least quadratically. This new outlier rejection formulation enables us to evaluate pose estimation for 1-to-many matches and to surpass the state-ofthe-art. At the same time, we show that using more matches does not automatically lead to a better performance.

A human and group behavior simulation evaluation framework utilizing composition and video analysis

“…Due to this, it is important to line up the perspective plane with the background to give the illusion of the agents walking through the scene. This alignment can be performed manually using the position and orientation of the camera or automatically using camera calibration techniques …”

“…This alignment can be performed manually using the position and orientation of the camera or automatically using camera calibration techniques. 50,51 Sample agents are then placed in the scene at various locations to ensure that the perspective and scaling parameters of the agents are appropriate to the scene. Figure 7 demonstrates this with agents in blue positioned at different locations in the scene.…”

Cascaded Parallel Filtering for Memory-Efficient Image-Based Localization

“…In feature-wise filtering, we reformulate a traditional match scoring function [16] with a bilateral Hamming ratio test to better evaluate the distinctiveness of matches. In visibility-arXiv:1908.06141v1 [cs.CV] 16 Aug 2019 Method Feature Type Compactness Match Filtering Prior-free SR Feature-wise Visibility-wise Geometry-wise AS [31] SIFT Strict WPE [21] SIFT Relaxed CSL [37] SIFT Relaxed * CPV [41] SIFT Relaxed * Hyperpoints [29] SIFT Relaxed In RPE EGM [23] SIFT+Binary Relaxed TC [6] SIFT Relaxed SMC [38] SIFT Relaxed * Our method Binary Relaxed Before RPE Table 1: Comparison between our method and other structure-based IBL methods. * means that the vertical direction of camera is known in advance, SR represents Spatial Reconfiguration and RPE represents RANSAC-based Pose Estimation.…”

“…The matches that are co-visible frequently with seed matches are prioritized to accelerate the matching process. Recent works [6,21,28,37,38,41] commonly relax the feature-wise filtering criterion to preserve more correct matches and shift the filtering task to visibility or geometry tools. Li et al introduce a RANSAC sampling strategy by prioritizing samples with frequent co-visibility [21].…”

SIFT Saliency Analysis for Matching Repetitive Structures

“…To avoid estimating the parameters of image conditions, we apply both the simulated images and the images provided by the INRIA dataset [19] for saliency computation in the Monte-Carlo method shown in Section 2.2. Thus, the LeaveOne-Out Cross-Validation (LOOCV) [22] test shown in Algorithm 1 is designed. To reduce variability, all possible rounds of cross-validation are performed for each scene in INRIA dataset [19] and the validation results are averaged over the rounds.…”