5 research outputs found
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
Long-term camera re-localization is an important task with numerous computer
vision and robotics applications. Whilst various outdoor benchmarks exist that
target lighting, weather and seasonal changes, far less attention has been paid
to appearance changes that occur indoors. This has led to a mismatch between
popular indoor benchmarks, which focus on static scenes, and indoor
environments that are of interest for many real-world applications. 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. We propose new metrics for
evaluating camera re-localization and explore how state-of-the-art camera
re-localizers perform according to these metrics. We also examine in detail how
different types of scene change affect the performance of different methods,
based on novel ways of detecting such changes in a given RGB-D frame. Our
results clearly show that long-term indoor re-localization is an unsolved
problem. Our benchmark and tools are publicly available at
waldjohannau.github.io/RIO10Comment: ECCV 2020, project website https://waldjohannau.github.io/RIO1
Image Matching across Wide Baselines: From Paper to Practice
We introduce a comprehensive benchmark for local features and robust
estimation algorithms, focusing on the downstream task -- the accuracy of the
reconstructed camera pose -- as our primary metric. Our pipeline's modular
structure allows easy integration, configuration, and combination of different
methods and heuristics. This is demonstrated by embedding dozens of popular
algorithms and evaluating them, from seminal works to the cutting edge of
machine learning research. We show that with proper settings, classical
solutions may still outperform the perceived state of the art.
Besides establishing the actual state of the art, the conducted experiments
reveal unexpected properties of Structure from Motion (SfM) pipelines that can
help improve their performance, for both algorithmic and learned methods. Data
and code are online https://github.com/vcg-uvic/image-matching-benchmark,
providing an easy-to-use and flexible framework for the benchmarking of local
features and robust estimation methods, both alongside and against
top-performing methods. This work provides a basis for the Image Matching
Challenge https://vision.uvic.ca/image-matching-challenge.Comment: Added: KeyNet-SOSNet, AffNet-HardNet, TFeat, MKD from korni