Zero-Shot Transfer 3D Point Cloud Classification

11 papers with code • 3 benchmarks • 2 datasets

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Libraries

Use these libraries to find Zero-Shot Transfer 3D Point Cloud Classification models and implementations
2 papers
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2 papers
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Most implemented papers

Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

qizekun/ReCon 5 Feb 2023

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 27 Feb 2024

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 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 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 10 Oct 2023

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 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

salesforce/ulip CVPR 2023

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 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

TencentARC/ViT-Lens 20 Aug 2023

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

ucsc-vlaa/mixcon3d CVPR 2024

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.