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Found 5 papers, 1 papers with code
DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces
Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.
Deep Implicit Surface Point Prediction Networks
Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.
Unsupervised Segmentation in Real-World Images via Spelke Object Inference
Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision.
Unifying (Machine) Vision via Counterfactual World Modeling
Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets.
Counterfactual World Modeling for Physical Dynamics Understanding
Third, the counterfactual modeling capability enables the design of counterfactual queries to extract vision structures similar to keypoints, optical flows, and segmentations, which are useful for dynamics understanding.
