Zero-shot 3D classification
9 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
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 V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
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
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.
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.
ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
It achieves a new SOTA of 50. 6% (top-1) on Objaverse-LVIS and 84. 7% (top-1) on ModelNet40 in zero-shot classification.
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
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.
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
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.
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.