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Found 3 papers, 3 papers with code
Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
In this way, each 3D shape with arbitrary views is represented by a fixed number of canonical view features, which are further aggregated to generate a rich and robust 3D shape representation for shape recognition.
Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification
In this paper, rather than relying on texture based information, we propose to improve the robustness of person ReID against clothing texture by exploiting the information of a person's 3D shape.
Ranked #4 on
Person Re-Identification
on PRCC
Deep Graph Matching under Quadratic Constraint
Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes.
Ranked #7 on
Graph Matching
on Willow Object Class
