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Found 10 papers, 2 papers with code
Multi-view Image Prompted Multi-view Diffusion for Improved 3D Generation
Using image as prompts for 3D generation demonstrate particularly strong performances compared to using text prompts alone, for images provide a more intuitive guidance for the 3D generation process.
Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform
Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics.
Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences
Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e. g., the Janus face problem or the content drift problem, in zero-shot text-to-3D models.
Efficient Semantic Matching with Hypercolumn Correlation
Recent studies show that leveraging the match-wise relationships within the 4D correlation map yields significant improvements in establishing semantic correspondences - but at the cost of increased computation and latency.
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely varying appearances.
Learning Rotation-Equivariant Features for Visual Correspondence
The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension.
TransforMatcher: Match-to-Match Attention for Semantic Correspondence
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints or intra-class variations.
Ranked #10 on
Semantic correspondence
on SPair-71k
Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence
To validate the proposed techniques, we develop the neural network with CHM layers that perform convolutional matching in the space of translation and scaling.
Deep Hough Voting for Robust Global Registration
We then construct a set of triplets of correspondences to cast votes on the 6D Hough space, representing the transformation parameters in sparse tensors.
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
Data is one of the most important factors in machine learning.
