18 papers with code • 1 benchmarks • 1 datasets
We present a new method for scene agnostic camera localization using dense scene matching (DSM), where a cost volume is constructed between a query image and a scene.
In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms.
We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.
Ranked #1 on Camera Localization on Aachen Day-Night benchmark
Additionally, the dropout module enables the pose regressor to output multiple hypotheses from which the uncertainty of pose estimates can be quantified and leveraged in the following uncertainty-aware pose-graph optimization to improve the robustness further.
In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment.
The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result.
In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks.
Ranked #1 on Horizon Line Estimation on Horizon Lines in the Wild