Generative 3D Object Classification
5 papers with code • 2 benchmarks • 2 datasets
The task of generative 3D object classification involves prompting the model to generate the object type from its point cloud, distinguishing it from discriminative models that directly classify objects based on probability comparisons.
Benchmarks
These leaderboards are used to track progress in Generative 3D Object Classification
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Libraries
Use these libraries to find Generative 3D Object Classification models and implementations
ModelNet
Objaverse
Most implemented papers
3D-LLM: Injecting the 3D World into Large Language Models
umass-foundation-model/3d-llm
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NeurIPS 2023
Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs.
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
ziyuguo99/point-bind_point-llm
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We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video.
PointLLM: Empowering Large Language Models to Understand Point Clouds
The unprecedented advancements in Large Language Models (LLMs) have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding.
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.
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
Notably, MiniGPT-3D gains an 8. 12 increase on GPT-4 evaluation score for the challenging object captioning task compared to ShapeLLM-13B, while the latter costs 160 total GPU-hours on 8 A800.
