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Found 5 papers, 1 papers with code
Sur2f: A Hybrid Representation for High-Quality and Efficient Surface Reconstruction from Multi-view Images
Technically, we learn two parallel streams of an implicit signed distance field and an explicit surrogate surface Sur2f mesh, and unify volume rendering of the implicit signed distance function (SDF) and surface rendering of the surrogate mesh with a shared, neural shader; the unified shading promotes their convergence to the same, underlying surface.
HelixSurf: A Robust and Efficient Neural Implicit Surface Learning of Indoor Scenes with Iterative Intertwined Regularization
Recovery of an underlying scene geometry from multiview images stands as a long-time challenge in computer vision research.
Surface Reconstruction from Point Clouds: A Survey and a Benchmark
For example, while deep learning methods are increasingly popular, our systematic studies suggest that, surprisingly, a few classical methods perform even better in terms of both robustness and generalization; our studies also suggest that the practical challenges of misalignment of point sets from multi-view scanning, missing of surface points, and point outliers remain unsolved by all the existing surface reconstruction methods.
6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-based Instance Representation Learning
Specifically, a novel two-stream encoder-decoder framework is dedicated to exploring complex and powerful instance representations from RGB images, point clouds and categorical shape priors.
Domestic activities clustering from audio recordings using convolutional capsule autoencoder network
Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster.
