Exceptional Point
Exceptional points, in the context of these papers, refer to the use of point-based representations and their fusion with other data modalities (images, lines, text) to improve various computer vision and machine learning tasks. Current research focuses on developing efficient algorithms and model architectures, such as transformers and diffusion models, to process and integrate these point features for applications like 3D object detection, pose estimation, and semantic segmentation. This research is significant because it addresses challenges in handling unstructured data and improves the accuracy and efficiency of numerous applications, ranging from robotics and autonomous driving to medical image analysis.
Papers
Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping
Qianxu Wang, Congyue Deng, Tyler Ga Wei Lum, Yuanpei Chen, Yaodong Yang, Jeannette Bohg, Yixin Zhu, Leonidas Guibas
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation
Ruiyu Xiao, Lei Wu, Yuhang Gou, Weinan Zhang, Ting Liu
More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding
Yuan Tang, Xu Han, Xianzhi Li, Qiao Yu, Jinfeng Xu, Yixue Hao, Long Hu, Min Chen
Str-L Pose: Integrating Point and Structured Line for Relative Pose Estimation in Dual-Graph
Zherong Zhang, Chunyu Lin, Shujuan Huang, Shangrong Yang, Yao Zhao
TAPTRv2: Attention-based Position Update Improves Tracking Any Point
Hongyang Li, Hao Zhang, Shilong Liu, Zhaoyang Zeng, Feng Li, Tianhe Ren, Bohan Li, Lei Zhang
Optimal camera-robot pose estimation in linear time from points and lines
Guangyang Zeng, Biqiang Mu, Qingcheng Zeng, Yuchen Song, Chulin Dai, Guodong Shi, Junfeng Wu