![]() ![]() Point cloud-conditioned image generation. Learned shape representations can also be integrated with off-the-shelfĬLIP-based models for various applications, such as point cloud captioning and SANCTUM - 3D - Side by Side - 1080P - DivX HD - AC3 6ch - By NicOdger.avi Video Create Time: Files: 1 Total size: 6. Due to their alignment with CLIP embeddings, our (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3DĪnd image-3D interactions. 3D Sanctum models are ready for animation, games and VR / AR projects. Our learned embeddings encode a wide range of visual and semantic concepts 3 3D Sanctum models available for download. Performing on par with some fully-supervised methods. ModelNet40, outperforming previous zero-shot baseline methods by 20% and ![]() OpenShape also achieves an accuracy of 85.3% on Of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less thanġ0% for existing methods. Specifically, OpenShape achieves a zero-shot accuracy We evaluate OpenShape on zero-shot 3DĬlassification benchmarks and demonstrate its superior capabilities for Scaling 3D backbone networks and introduce a novel hard negative mining moduleįor more efficient training. We also explore and compare strategies for Multiple 3D datasets and propose several strategies to automatically filter andĮnrich noisy text descriptions. To achieve this, we scale up training data by ensembling With a specific focus on scaling up 3D representations to enable open-world 3D Multi-modal contrastive learning framework for representation alignment, but Representations of text, image, and point clouds. Download a PDF of the paper titled OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding, by Minghua Liu and 8 other authors Download PDF Abstract: We introduce OpenShape, a method for learning multi-modal joint ![]()
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