We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the num...We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. Despite raising attention to 3D registration with complex or low overlapping patterns, few methods consider shape assembly with rare overlaps. To address this problem, we present a novel framework inspired by solving puzzles, named PuzzleNet, which conducts multi-task learning by leveraging both 3D alignment and boundary information. Specifically, we design an end-to-end neural network based on a point cloud transformer with two-way branches for estimating rigid transformation and predicting boundaries simultaneously. The framework is then naturally extended to reassemble multiple pieces into a full shape by using an iterative greedy approach based on the distance between each pair of candidate-matched pieces. To train and evaluate PuzzleNet, we construct two datasets, named ModelPuzzle and DublinPuzzle, based on a real-world urban scan dataset (DublinCity) and a synthetic CAD dataset (ModelNet40) respectively. Experiments demonstrate our effectiveness in solving 3D shape assembly for multiple pieces with arbitrary geometry and inconsistent semantics. Our method surpasses state-of-the-art algorithms by more than 10 times in rotation metrics and four times in translation metrics.展开更多
The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a chal...The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a challenging task due to the vast amount of historical data available. By leveraging neural networks to identify and parse floor plans, the process of converting these images into electronic materials can be significantly streamlined. In this paper, we present a novel learning framework for automatically parsing floor plan images. Our key insight is that the room type text is very common and crucial in floor plan images as it identifies the important semantic information of the corresponding room. However, this clue is rarely considered in previous learning-based methods. In contrast, we propose the Row and Column network (RC-Net) for recognizing floor plan elements by integrating the text feature. Specifically, we add the text feature branch in the network to extract text features corresponding to the room type for the guidance of room type predictions. More importantly, we formulate the Row and Column constraint module (RC constraint module) to share and constrain features across the entire row and column of the feature maps to ensure that only one type is predicted in each room as much as possible, making the segmentation boundaries between different rooms more regular and cleaner. Extensive experiments on three benchmark datasets validate that our framework substantially outperforms other state-of-the-art approaches in terms of the metrics of FWIoU, mACC and mIoU.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.U22B2034,62172416,U21A20515,62172415,62271467the Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant No.2022131.
文摘We address the 3D shape assembly of multiple geometric pieces without overlaps, a scenario often encountered in 3D shape design, field archeology, and robotics. Existing methods depend on strong assumptions on the number of shape pieces and coherent geometry or semantics of shape pieces. Despite raising attention to 3D registration with complex or low overlapping patterns, few methods consider shape assembly with rare overlaps. To address this problem, we present a novel framework inspired by solving puzzles, named PuzzleNet, which conducts multi-task learning by leveraging both 3D alignment and boundary information. Specifically, we design an end-to-end neural network based on a point cloud transformer with two-way branches for estimating rigid transformation and predicting boundaries simultaneously. The framework is then naturally extended to reassemble multiple pieces into a full shape by using an iterative greedy approach based on the distance between each pair of candidate-matched pieces. To train and evaluate PuzzleNet, we construct two datasets, named ModelPuzzle and DublinPuzzle, based on a real-world urban scan dataset (DublinCity) and a synthetic CAD dataset (ModelNet40) respectively. Experiments demonstrate our effectiveness in solving 3D shape assembly for multiple pieces with arbitrary geometry and inconsistent semantics. Our method surpasses state-of-the-art algorithms by more than 10 times in rotation metrics and four times in translation metrics.
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20515,62172416,52175493,U2003109,61972459,and 62102414the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2022131).
文摘The popularity of online home design and floor plan customization has been steadily increasing. However, the manual conversion of floor plan images from books or paper materials into electronic resources can be a challenging task due to the vast amount of historical data available. By leveraging neural networks to identify and parse floor plans, the process of converting these images into electronic materials can be significantly streamlined. In this paper, we present a novel learning framework for automatically parsing floor plan images. Our key insight is that the room type text is very common and crucial in floor plan images as it identifies the important semantic information of the corresponding room. However, this clue is rarely considered in previous learning-based methods. In contrast, we propose the Row and Column network (RC-Net) for recognizing floor plan elements by integrating the text feature. Specifically, we add the text feature branch in the network to extract text features corresponding to the room type for the guidance of room type predictions. More importantly, we formulate the Row and Column constraint module (RC constraint module) to share and constrain features across the entire row and column of the feature maps to ensure that only one type is predicted in each room as much as possible, making the segmentation boundaries between different rooms more regular and cleaner. Extensive experiments on three benchmark datasets validate that our framework substantially outperforms other state-of-the-art approaches in terms of the metrics of FWIoU, mACC and mIoU.
