Jiahao Li

Jiahao Li, Changhao Zhang, Ziyao Xu et al.

In this paper, we propose a novel learning-based pipeline for partially overlapping 3D point cloud registration. The proposed model includes an iterative distance-aware similarity matrix convolution module to incorporate information from both the feature and Euclidean space into the pairwise point matching process. These convolution layers learn to match points based on joint information of the entire geometric features and Euclidean offset for each point pair, overcoming the disadvantage of matching by simply taking the inner product of feature vectors. Furthermore, a two-stage learnable point elimination technique is presented to improve computational efficiency and reduce false positive correspondence pairs. A novel mutual-supervision loss is proposed to train the model without extra annotations of keypoints. The pipeline can be easily integrated with both traditional (e.g. FPFH) and learning-based features. Experiments on partially overlapping and noisy point cloud registration show that our method outperforms the current state-of-the-art, while being more computationally efficient. Code is publicly available at https://github.com/jiahaowork/idam.

Jiahao Li, Meilin Cui, Jeeeun Kim et al.

Reconfiguring shapes of objects enables transforming existing passive objects with robotic functionalities, e.g., a transformable coffee cup holder can be attached to a chair's armrest, a piggy bank can reach out an arm to 'steal' coins. Despite the advance in end-user 3D design and fabrication, it remains challenging for non-experts to create such 'transformables' using existing tools due to the requirement of specific engineering knowledge such as mechanisms and robotic design. We present Romeo -- a design tool for creating transformables to robotically augment objects' default functionalities. Romeo allows users to transform an object into a robotic arm by expressing at a high level what type of task is expected. Users can select which part of the object to be transformed, specify motion points in space for the transformed part to follow and the corresponding action to be taken. Romeo then automatically generates a robotic arm embedded in the transformable part ready for fabrication. A design session validated this tool where participants used Romeo to accomplish controlled design tasks and to open-endedly create coin-stealing piggy banks by transforming 3D objects of their own choice.