Zero-Shot Transfer 3D Point Cloud Classification
10 papers with code • 3 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Zero-Shot Transfer 3D Point Cloud Classification
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Libraries
Use these libraries to find Zero-Shot Transfer 3D Point Cloud Classification models and implementations
ModelNet
ScanObjectNN
Most implemented papers
Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
qizekun/ReCon
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This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
qizekun/ShapeLLM
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This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
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.
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
Then, ULIP learns a 3D representation space aligned with the common image-text space, using a small number of automatically synthesized triplets.
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Colin97/OpenShape_code
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NeurIPS 2023
Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.
ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights
A well-trained lens with a ViT backbone has the potential to serve as one of these foundation models, supervising the learning of subsequent modalities.
Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training
Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i. e., aligning point cloud representation to image and text embedding space individually.
