Search Results for author:
Found 17 papers, 8 papers with code
Privacy Preserving Structure-from-Motion
The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns.
Robust Incremental Structure-from-Motion with Hybrid Features
In this work, we introduce an incremental SfM system that, in addition to points, leverages lines and their structured geometric relations.
Global Structure-from-Motion Revisited
Recovering 3D structure and camera motion from images has been a long-standing focus of computer vision research and is known as Structure-from-Motion (SfM).
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction
Using these main contributions, our method can enable scenarios with real-time constraints and can scale to arbitrary scene sizes by processing and updating the scene only in a local region defined by the new measurement.
Privacy Preserving Localization via Coordinate Permutations
Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps.
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes.
Privacy Preserving Partial Localization
We propose a principled approach overcoming these limitations, based on two observations.
Reconstructing and grounding narrated instructional videos in 3D
Contrary to the standard scenario of instance-level 3D reconstruction, where identical objects or scenes are present in all views, objects in different instructional videos may have large appearance variations given varying conditions and versions of the same product.
Cross-Descriptor Visual Localization and Mapping
Visual localization and mapping is the key technology underlying the majority of mixed reality and robotics systems.
NeuralFusion: Online Depth Fusion in Latent Space
We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space.
HoloLens 2 Research Mode as a Tool for Computer Vision Research
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings
Many computer vision systems require users to upload image features to the cloud for processing and storage.
Multi-View Optimization of Local Feature Geometry
In this work, we address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry.
RoutedFusion: Learning Real-time Depth Map Fusion
To this end, we present a novel real-time capable machine learning-based method for depth map fusion.
Privacy Preserving Image-Based Localization
Current localization systems rely on the persistent storage of 3D point clouds of the scene to enable camera pose estimation, but such data reveals potentially sensitive scene information.
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision.
scikit-image: Image processing in Python
scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications.
