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Found 9 papers, 0 papers with code
Light Field Super-Resolution Via Graph-Based Regularization
We adopt a multi-frame alike super-resolution approach, where the complementary information in the different light field views is used to augment the spatial resolution of the whole light field.
DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction
Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions.
Joint Graph-based Depth Refinement and Normal Estimation
Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings.
BP-MVSNet: Belief-Propagation-Layers for Multi-View-Stereo
We therefore show how we can calculate a normalization based on the expected 3D error, which we can then use to normalize the label jumps in the CRF.
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions
We present a novel deep-learning-based method for Multi-View Stereo.
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MD-Net: Multi-Detector for Local Feature Extraction
In order to lower the computational cost of the matching phase, we propose a deep feature extraction network capable of detecting a predefined number of complementary sets of keypoints at each image.
DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo
We denote our method DELS-MVS: Deep Epipolar Line Search Multi-View Stereo.
S-TREK: Sequential Translation and Rotation Equivariant Keypoints for local feature extraction
In this work we introduce S-TREK, a novel local feature extractor that combines a deep keypoint detector, which is both translation and rotation equivariant by design, with a lightweight deep descriptor extractor.
Exploiting Multiple Priors for Neural 3D Indoor Reconstruction
In this work, we propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor environments, while relying only on images.
