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Img2CAD: Conditioned 3D CAD Model Generation from Single Image with Structured Visual Geometry

Autor: Chen, Tianrun, Yu, Chunan, Hu, Yuanqi, Li, Jing, Xu, Tao, Cao, Runlong, Zhu, Lanyun, Zang, Ying, Zhang, Yong, Li, Zejian, Sun, Linyun

In this paper, we propose Img2CAD, the first approach to our knowledge that uses 2D image inputs to generate CAD models with editable parameters. Unlike existing AI methods for 3D model generation using text or image inputs often rely on mesh-based r

Externí odkaz: http://arxiv.org/abs/2410.03417

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SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More

Autor: Chen, Tianrun, Lu, Ankang, Zhu, Lanyun, Ding, Chaotao, Yu, Chunan, Ji, Deyi, Li, Zejian, Sun, Lingyun, Mao, Papa, Zang, Ying

The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable success in diverse image segmentation scenarios. Despite its advancements,

Externí odkaz: http://arxiv.org/abs/2408.04579

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Reasoning3D -- Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models

Autor: Chen, Tianrun, Yu, Chunan, Li, Jing, Zhang, Jianqi, Zhu, Lanyun, Ji, Deyi, Zhang, Yong, Zang, Ying, Li, Zejian, Sun, Lingyun

In this paper, we introduce a new task: Zero-Shot 3D Reasoning Segmentation for parts searching and localization for objects, which is a new paradigm to 3D segmentation that transcends limitations for previous category-specific 3D semantic segmentati

Externí odkaz: http://arxiv.org/abs/2405.19326

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