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Found 14 papers, 9 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.
Two-View Geometry Scoring Without Correspondences
As a remedy, we propose the Fundamental Scoring Network (FSNet), which infers a score for a pair of overlapping images and any proposed fundamental matrix.
DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models
During NeRF training, random RGBD patches are rendered and the estimated gradient of the log-likelihood is backpropagated to the color and density fields.
Map-free Visual Relocalization: Metric Pose Relative to a Single Image
Can we relocalize in a scene represented by a single reference image?
Single Image Depth Prediction with Wavelet Decomposition
We present a novel method for predicting accurate depths from monocular images with high efficiency.
Learning to Predict Repeatability of Interest Points
Many robotics applications require interest points that are highly repeatable under varying viewpoints and lighting conditions.
Single-Image Depth Prediction Makes Feature Matching Easier
Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines.
Image Stylization for Robust Features
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks.
Predicting Visual Overlap of Images Through Interpretable Non-Metric Box Embeddings
Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features.
Learning Stereo from Single Images
We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs.
Self-Supervised Monocular Depth Hints
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data.
Ranked #2 on
Monocular Depth Estimation
on VA (Virtual Apartment)
Interpretable Transformations with Encoder-Decoder Networks
We propose a simple method to construct a deep feature space, with explicitly disentangled representations of several known transformations.
Modeling Object Appearance Using Context-Conditioned Component Analysis
In this work we remove the image space alignment limitations of existing subspace models by conditioning the models on a shape dependent context that allows for the complex, non-linear structure of the appearance of the visual object to be captured and shared.
