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Found 14 papers, 4 papers with code
Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer
We address the task of estimating camera parameters from a set of images depicting a scene.
Map-Relative Pose Regression for Visual Re-Localization
We present a new approach to pose regression, map-relative pose regression (marepo), that satisfies the data hunger of the pose regression network in a scene-agnostic fashion.
Accelerated Coordinate Encoding: Learning to Relocalize in Minutes using RGB and Poses
We start from the obvious: a relocalization network can be split in a scene-agnostic feature backbone, and a scene-specific prediction head.
Recurrently Estimating Reflective Symmetry Planes from Partial Pointclouds
Additionally, we show that it can be deployed on partial scans of objects in a real-world pipeline to improve the outputs of a 3D object detector.
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
In this paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes.
ShardNet: One Filter Set to Rule Them All
Deep CNNs have achieved state-of-the-art performance for numerous machine learning and computer vision tasks in recent years, but as they have become increasingly deep, the number of parameters they use has also increased, making them hard to deploy in memory-constrained environments and difficult to interpret.
Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC
Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs.
Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation
Many applications require a camera to be relocalised online, without expensive offline training on the target scene.
R$^3$SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems
Stereo depth estimation is used for many computer vision applications.
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.
Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation
Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases.
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
Representing the reconstruction volumetrically as a TSDF leads to most of the simplicity and efficiency that can be achieved with GPU implementations of these systems.
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation.
Volume-based Semantic Labeling with Signed Distance Functions
Research works on the two topics of Semantic Segmentation and SLAM (Simultaneous Localization and Mapping) have been following separate tracks.
