PointLLM
[ECCV 2024 Best Paper Candidate & TPAMI 2025] PointLLM: Empowering Large Language Models to Understand Point Clouds
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We introduce PointLLM, a multi-modal large language model capable of understanding colored point clouds of objects. It perceives object types, geometric structures, and appearance without concerns for ambiguous depth, occlusion, or viewpoint dependency. We collect a novel dataset comprising 660K simple and 70K complex point-text instruction pairs to enable a two-stage training strategy. To rigorously evaluate our model's perceptual abilities and its generalization capabilities, we establish two benchmarks: Generative 3D Object Classification and 3D Object Captioning, assessed through three different evaluation methods.
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Intern Robotics
Building inclusive infrastructure for Embodied AI, from Shanghai AI Lab
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