In this paper, we study the problem o f 3D object de tection from stereo images, in which the key challenge is how to effectively utilize stereo information. Different from previous methods using pixel -level  3D object detection aims at localizing amodal 3D bound ing boxes of objects of specific classes in the 3D space [ , , 18,29]. The detection task is relatively easier [ , ,15] when active 3D scan data are provided. However, the active scan data are of high cost and limited in scalability. We address the problem of 3D detection from passive imagery data, which require only low-cost hardware, adapt to differ ent scales of objects, and offer fruitful semantic features. Monocular 3D detection with a single RGB image is highly ill-posed because of the ambiguous mapping from 2D images to 3D geometries, but still attracts research ef forts [ , 3( ] because of its simplicity of design. Adding more input views can provide more information for 3D reasoning, which is ubiquitously demonstrated in the tra ditional multi-view geometry [ ] area by finding dense patch-level correspondences for points and then estimate their 3D locations by triangulation. The geometric meth ods deal with patch-level points with local features, while no semantic clues at object-level are considered. Stereo data with pairs of images are more suitable for 3D detection, since the disparities between left and right images can reveal spacial variance, especially in the depth dimen sion. While extensive work [ , 2' , ] has been done on deep-learning based stereo matching, their main focus is on the pixel level rather than object level. 3DOP [ ] uses stereo images for 3D object detection and achieves state-of-the-art results. Nevertheless, later we show that we can obtain com parable results using only a monocular image, by properly placing 3D anchors and extending the region-proposal net work (RPN) [ ] to 3D. Therefore, we are motivated to re consider how to better exploit the potential of stereo images for accurate 3D object detection. In this paper, we propose the stereo Triangulation Learning Network (TLNet) for 3D object detection from stereo images, which is free of computing pixel-level depth maps and can be easily integrated into the baseline monoc ular detector. The key idea is to use a 3D anchor box to explicitly construct object-level geometric correspondences of its two projections on a pair of stereo images, from which the network learns to triangulate a targeted object near the anchor. In the proposed TLNet, we introduce an 978-1-7281-3293-
TLNet REF introduces a triangulation based learning network to pair detected regions of interests between left and right images.
Triangulation Learning Network: From Monocular to Stereo 3D Object Detection
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science