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Found 4 papers, 3 papers with code
Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels
Thereby we demonstrate that 2D vision language models such as CLIP can be used to complement 3D representation learning to improve classification performance without the need for expensive class annotations.
DETER: Detecting Edited Regions for Deterring Generative Manipulations
While face swapping and attribute editing are performed on similar face regions such as eyes and nose, the inpainting operation can be performed on random image regions, removing the spurious correlations of previous datasets.
3DLatNav: Navigating Generative Latent Spaces for Semantic-Aware 3D Object Manipulation
However, most models do not offer controllability to manipulate the shape semantics of component object parts without extensive semantic attribute labels or other reference point clouds.
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
3D Object Classification
3D Point Cloud Linear Classification
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