Incidental scene text detection, especially for multioriented text regions, is one of the most challenging tasks in many computer vision applications. Different from the common object detection task, scene text often suffers from a large variance of aspect ratio, scale, and orientation. To solve this problem, we propose a novel end-to-end scene text detector IncepText from an instance-aware segmentation perspective. We design a novel Inception-Text module and introduce deformable PSROI pooling to deal with multi-oriented text detection. Extensive experiments on ICDAR2015, RCTW-17, and MSRA-TD500 datasets demonstrate our method's superiority in terms of both effectiveness and efficiency. Our proposed method achieves 1st place result on ICDAR2015 challenge and the state-ofthe-art performance on other datasets. Moreover, we have released our implementation as an OCR product which is available for public access. 1
Abstract-Different from previous tree modeling approaches, our method is based on the idea of making tree reconstruction as quick as possible and simplifying the representation of final results while keeping the tree model visually acceptable. Each tree is represented by Billboard model. We first get the shape mask of a tree by projecting LiDAR point cloud onto 2D camera plane. Then we use the shape fitting method to obtain the corresponding rotation axes and bounding boxes for the main trunk and tree crown. We get the corresponding texture and correct the misalignment artifacts by texture completion. Finally, we rotate each textured polygon around the rotation axis to a certain degree. We demonstrate the effectiveness of our system with some LiDAR data sets and compare our tree modeling scheme with other state-of-the-art reconstruction algorithms to show its advantages in terms of speed and memory footprint.
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