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Found 13 papers, 7 papers with code
V-FUSE: Volumetric Depth Map Fusion with Long-Range Constraints
We introduce a learning-based depth map fusion framework that accepts a set of depth and confidence maps generated by a Multi-View Stereo (MVS) algorithm as input and improves them.
EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
Our method incorporates an Error-state Kalman Filter (ESKF) to estimate gyroscope bias and correct rotation estimates from monocular SLAM, overcoming dependence on pure monocular SLAM for rotation estimation.
Real-Time Dense 3D Mapping of Underwater Environments
To address this problem, we propose to use SVIn2, a robust VIO method, together with a real-time 3D reconstruction pipeline.
Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation
We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching.
Single-Camera 3D Head Fitting for Mixed Reality Clinical Applications
We address the problem of estimating the shape of a person's head, defined as the geometry of the complete head surface, from a video taken with a single moving camera, and determining the alignment of the fitted 3D head for all video frames, irrespective of the person's pose.
Do End-to-end Stereo Algorithms Under-utilize Information?
In this paper, we show how deep adaptive filtering and differentiable semi-global aggregation can be integrated in existing 2D and 3D convolutional networks for end-to-end stereo matching, leading to improved accuracy.
Matching-space Stereo Networks for Cross-domain Generalization
End-to-end deep networks represent the state of the art for stereo matching.
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey
Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research.
Oriented Point Sampling for Plane Detection in Unorganized Point Clouds
We named this method Oriented Point Sampling (OPS) to contrast with more conventional techniques that require the sampling of three unoriented points to generate plane hypotheses.
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
The success of these methods is due to the availability of training data with ground truth; training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions.
Exact Bias Correction and Covariance Estimation for Stereo Vision
We present an approach for correcting the bias in 3D reconstruction of points imaged by a calibrated stereo rig.
Learning to Detect Ground Control Points for Improving the Accuracy of Stereo Matching
While machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success.
3D Interest Point Detection via Discriminative Learning
Sometimes points with high curvature may be desirable, while in other cases high curvature may be an indication of noise.
