Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds
Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds
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DOI：
10.1109/cvpr52729.2023.02025
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Shao-Wei Liu;Saurabh Gupta;Shenlong Wang
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文献类型：
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作者：
Shao-Wei Liu;Saurabh Gupta;Shenlong Wang
我们建立rearticulable模型的任意日常人造物体包含任意数量的部分，连接在一起，以任意的方式通过1度的自由关节。给定这些日常物体的点云视频，我们的方法识别不同的物体部分，哪些部分连接到其他部分，以及连接每个部分对的关节的属性。我们这样做，通过共同优化的部分分割，变换和运动学使用一种新的能量最小化框架。我们推断的动画模型，使重定向到新的姿态与稀疏点对应的指导。我们在一个新的关节机器人数据集和具有常见日常对象的Sapiens数据集上测试了我们的方法。实验表明，我们的方法优于两个领先的各种指标的工作。
We build rearticulable models for arbitrary everyday man-made objects containing an arbitrary number of parts that are connected together in arbitrary ways via 1 degree-of-freedom joints. Given point cloud videos of such everyday objects, our method identifies the distinct object parts, what parts are connected to what other parts, and the properties of the joints connecting each part pair. We do this by jointly optimizing the part segmentation, transformation, and kinematics using a novel energy minimization frame-work. Our inferred animatable models, enables retargeting to novel poses with sparse point correspondences guidance. We test our method on a new articulating robot dataset, and the Sapiens dataset with common daily objects. Experiments show that our method outperforms two leading prior works on various metrics.