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Found 5 papers, 0 papers with code
Complete Neural Networks for Complete Euclidean Graphs
Neural networks for point clouds, which respect their natural invariance to permutation and rigid motion, have enjoyed recent success in modeling geometric phenomena, from molecular dynamics to recommender systems.
Low Dimensional Invariant Embeddings for Universal Geometric Learning
We show that when a continuous family of semi-algebraic separating invariants is available, separation can be obtained by randomly selecting $2D+1 $ of these invariants.
Unique Geometry and Texture from Corresponding Image Patches
We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process.
Low-level Vision by Consensus in a Spatial Hierarchy of Regions
We introduce a multi-scale framework for low-level vision, where the goal is estimating physical scene values from image data---such as depth from stereo image pairs.
From Shading to Local Shape
We develop a framework for extracting a concise representation of the shape information available from diffuse shading in a small image patch.
