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Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting progress in 3D language understanding and generation.
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Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training
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Openflamingo: An open-source framework for training large autoregressive vision-language models
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Visionllm: Large language model is also an open-ended decoder for vision-centric tasks
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Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding
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