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Generative 3D Object Classification

3D Object Classification › Generative 3D Object Classification 13 평가 기록 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.

논문

PointLLM: Empowering Large Language Models to Understand Point Clouds

2308.16911 2023-08-31 이 페이지의 평가 4건

Objaverse (I): 56.50Objaverse (Average): 54.00Objaverse (C): 51.50

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

2402.17766 2024-02-27 이 페이지의 평가 4건

Objaverse (Average): 54.50

MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors

2405.01413 2024-05-02 이 페이지의 평가 2건

Objaverse (I): 60.00Objaverse (Average): 60.25Objaverse (C): 60.50

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

2309.00615 2023-09-01 이 페이지의 평가 2건

Objaverse (I): 6.00Objaverse (Average): 5.25Objaverse (C): 4.50

3D-LLM: Injecting the 3D World into Large Language Models

2307.12981 2023-07-24 이 페이지의 평가 1건

Objaverse (I): 49.00Objaverse (Average): 45.25Objaverse (C): 41.50