Search Results for author:
Found 8 papers, 3 papers with code
Optimizing Fiducial Marker Placement for Improved Visual Localization
Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene.
Learning To Detect Scene Landmarks for Camera Localization
Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible.
PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility
To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals.
PatchMatch-Based Neighborhood Consensus for Semantic Correspondence
We address estimating dense correspondences between two images depicting different but semantically related scenes.
gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors
Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera.
Improved Structure from Motion Using Fiducial Marker Matching
In this paper, we present an incremental structure from motion (SfM) algorithm that signiï¬cantly outperforms existing algorithms when ï¬ducial markers are present in the scene, and that matches the performance of existing algorithms when no markers are present.
ChromaTag: A Colored Marker and Fast Detection Algorithm
Current fiducial marker detection algorithms rely on marker IDs for false positive rejection.
Geometry-Informed Material Recognition
Our goal is to recognize material categories using images and geometry information.
