Zero-shot 3D Point Cloud Classification
6 papers with code • 2 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Zero-shot 3D Point Cloud Classification
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ModelNet
ScanNet
Most implemented papers
PointCLIP: Point Cloud Understanding by CLIP
zrrskywalker/pointclip
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CVPR 2022
On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
yangyangyang127/pointclip_v2
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ICCV 2023
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
Uni3D: Exploring Unified 3D Representation at Scale
baaivision/uni3d
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Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training
tyhuang0428/CLIP2Point
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ICCV 2023
To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification.
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
mr-neko/jm3d
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Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality.
OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation
When integrated with powerful 2D open-world models such as ODISE and GroundingDINO, excellent results were observed on open-vocabulary instance segmentation.
