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  • GitHub - baaivision Uni3D: [ICLR24 Spotlight] Uni3D: 3D Visual . . .
    We present Uni3D, a unified and scalable 3D pretraining framework for large-scale 3D representation learning, and explore its limits at the scale of one billion parameters Uni3D uses a 2D initialized ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features
  • Uni3D: Exploring Unified 3D Representation at Scale
    In this work, we present Uni3D, a 3D foundation model to explore the unified 3D representation at scale Uni3D uses a 2D initialized ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features
  • Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
    Inspired by such observation, we present a Uni3D which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data-level and taxonomy-level differences, respectively
  • Uni3D at main · baaivision Uni3D - GitHub
    We present Uni3D, a unified and scalable 3D pretraining framework for large-scale 3D representation learning, and explore its limits at the scale of one billion parameters Uni3D uses a 2D initialized ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features
  • Uni3D: Exploring Unified 3D Representation at Scale - ICLR
    In this work, we present Uni3D, a 3D foundation model to explore the unified 3D representation at scale Uni3D uses a 2D initialized ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features
  • Applications and Capabilities | baaivision Uni3D | DeepWiki
    Uni3D enables open-world scene understanding by applying its zero-shot capabilities to real-world 3D scenes This allows for identification and segmentation of objects in complex environments without prior training on specific scene types
  • baaivision Uni3D | DeepWiki
    Uni3D aligns 3D point cloud features with image-text features from CLIP, enabling zero-shot capabilities and cross-modal understanding at scales up to one billion parameters This overview introduces the key concepts, architecture, and capabilities of Uni3D
  • Uni3D-MoE: Scalable Multimodal 3D Scene Understanding via Mixture of . . .
    To address these challenges, we propose Uni3D-MoE, a sparse Mixture-of-Experts (MoE)-based 3D MLLM designed to enable adaptive 3D multimodal fusion Specifically, Uni3D-MoE integrates a comprehensive set of 3D modalities, including multi-view RGB and depth images, bird's-eye-view (BEV) maps, point clouds, and voxel representations
  • [2310. 06773] Uni3D: Exploring Unified 3D Representation at Scale - ar5iv
    In this work, we propose Uni3D, a unified and scalable 3D pretraining framework for large-scale 3D representation learning, and explore its limits at the scale of one billion parameters with a million 3D shapes and 10 million images paired with 70 million texts
  • Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
    Statistics-level Alignment: We design a dataset-specific BN layer that can replace BN module in 3D or 2D Backbone, to achieve an effective distribution of point cloud representations





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