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Deep Cuboid Detection: Beyond 2D Bounding Boxes [article]

Debidatta Dwibedi, Tomasz Malisiewicz, Vijay Badrinarayanan, Andrew Rabinovich
2016 arXiv   pre-print
We localize cuboids with a 2D bounding box, and simultaneously localize the cuboid's corners, effectively producing a 3D interpretation of box-like objects.  ...  We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects).  ...  In addition to the 2D bounding box enclosing the cuboid, we estimate the location of all 8 vertices.  ... 
arXiv:1611.10010v1 fatcat:lsff3atibngwrfzhnz7yvcb7ha

ODAM: Object Detection, Association, and Mapping using Posed RGB Video [article]

Kejie Li, Daniel DeTone, Steven Chen, Minh Vo, Ian Reid, Hamid Rezatofighi, Chris Sweeney, Julian Straub, Richard Newcombe
2021 arXiv   pre-print
The proposed system relies on a deep learning front-end to detect 3D objects from a given RGB frame and associate them to a global object-based map using a graph neural network (GNN).  ...  We present ODAM, a system for 3D Object Detection, Association, and Mapping using posed RGB videos.  ...  Extending beyond 3D oriented bounding boxes, several works focus on estimating dense object shapes via shape embedding [45] or CAD model retrieval [27] given posed RGB video.  ... 
arXiv:2108.10165v1 fatcat:i4fsf2dwdrbfbce4x33szuxgvq

Movement Tube Detection Network Integrating 3D CNN and Object Detection Framework to Detect Fall

Song Zou, Weidong Min, Lingfeng Liu, Qi Wang, Xiang Zhou
2021 Electronics  
The constrained movement tube, which encapsulates the person with a sequence of bounding boxes, has the merits of encapsulating the person closely and avoiding peripheral interference.  ...  A movement tube detection network integrating 3D CNN and object detection framework such as SSD is proposed to detect human fall with constrained movement tubes.  ...  of the union of bounding box b and bounding box v.  ... 
doi:10.3390/electronics10080898 fatcat:unysaqvb7rb6zd3uxd2hzthkzy

Reinforced Axial Refinement Network for Monocular 3D Object Detection [article]

Lijie Liu, Chufan Wu, Jiwen Lu, Lingxi Xie, Jie Zhou, Qi Tian
2020 arXiv   pre-print
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image.  ...  Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small  ...  (In each group, we also show the 2D Average Precision (2D AP) of 2D detection results, which is the upper bound of AOS).  ... 
arXiv:2008.13748v1 fatcat:5eorvtrt3fgtzmyhetqkxmbr4a

Human Detection and Segmentation via Multi-view Consensus [article]

Isinsu Katircioglu, Helge Rhodin, Jörg Spörri, Mathieu Salzmann, Pascal Fua
2021 arXiv   pre-print
Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data.  ...  This corresponds to one 2D bounding box for each view. 3. We compute the 3D center and object height that best agree with these 2D bounding boxes in a least-square sense. 4.  ...  Because we ultimately aim to perform single-view 2D detection and segmentation, our approach produces bounding boxes and segmentation masks for each individual view.  ... 
arXiv:2012.05119v2 fatcat:u42bu5v4xnhw7pgwtzjcfvmhu4

IoU Loss for 2D/3D Object Detection [article]

Dingfu Zhou, Jin Fang, Xibin Song, Chenye Guan, Junbo Yin, Yuchao Dai, Ruigang Yang
2019 arXiv   pre-print
However, during the training stage, the common distance loss (, L_1 or L_2) is often adopted as the loss function to minimize the discrepancy between the predicted and ground truth Bounding Box (Bbox).  ...  To eliminate the performance gap between training and testing, the IoU loss has been introduced for 2D object detection in and .  ...  IoU computation for 2D: axis-aligned and rotated bounding boxes, where the green and red represent the ground truth and predicted bounding box respectively.  ... 
arXiv:1908.03851v1 fatcat:jvhspf5acnejlgjngyre4xlsbe

A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Seong-heum Kim, Youngbae Hwang
2021 Electronics  
This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection.  ...  Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized.  ...  In practice, coarse cuboids are reported to have sufficient accuracy for determining the 3D bounding boxes of objects by refinement.  ... 
doi:10.3390/electronics10040517 fatcat:rziqhrkefvelpg3vgb6qxfprte

Human Detection and Segmentation via Multi-view Consensus

Isinsu Katircioglu, Helge Rhodin, Jorg Sporri, Mathieu Salzmann, Pascal Fua
2021 2021 IEEE/CVF International Conference on Computer Vision (ICCV)  
This corresponds to one 2D bounding box for each view. 3. We compute the 3D center and object height that best agree with these 2D bounding boxes in a least-square sense. 4.  ...  Because we ultimately aim to perform single-view 2D detection and segmentation, our approach produces bounding boxes and segmentation masks for each individual view.  ... 
doi:10.1109/iccv48922.2021.00285 fatcat:ascyrs54kff6xcptl4lyw2zg5i

Frustum PointNets for 3D Object Detection from RGB-D Data [article]

Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas
2018 arXiv   pre-print
Benefited from learning directly in raw point clouds, our method is also able to precisely estimate 3D bounding boxes even under strong occlusion or with very sparse points.  ...  Instead of solely relying on 3D proposals, our method leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even  ...  However, beyond getting 2D bounding boxes or pixel masks, 3D understanding is eagerly in demand in many applications such as autonomous driving and augmented reality (AR).  ... 
arXiv:1711.08488v2 fatcat:aatdkha3gzgcnoe4rpem6fggaa

Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images

Zhuo Deng, Longin Jan Latecki
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
This paper addresses the problem of amodal perception of 3D object detection.  ...  We propose a novel 3D object detection system that simultaneously predicts objects' 3D locations, physical sizes, and orientations in indoor scenes.  ...  [26] provides two kinds of 2D ground truth bounding boxes for NYUV2 dataset: 1) projected 2D bounding boxes by fitting visible point clouds, and 2) projected 2D bounding boxes from amodal 3D bounding  ... 
doi:10.1109/cvpr.2017.50 dblp:conf/cvpr/DengL17 fatcat:b7gxdyda4bauze2esq7fa5be5q

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection [article]

Garrick Brazil, Xiaoming Liu
2019 arXiv   pre-print
We leverage the geometric relationship of 2D and 3D perspectives, allowing 3D boxes to utilize well-known and powerful convolutional features generated in the image-space.  ...  We propose to reduce the gap by reformulating the monocular 3D detection problem as a standalone 3D region proposal network.  ...  The point clouds are then sampled using 2D bounding boxes generated from a separate 2D RPN.  ... 
arXiv:1907.06038v2 fatcat:pydrzhad6jg35ikzive5ze6qui

A General Pipeline for 3D Detection of Vehicles [article]

Xinxin Du, Marcelo H. Ang Jr., Sertac Karaman, Daniela Rus
2018 arXiv   pre-print
A two-stage convolutional neural network (CNN) is proposed to refine the detected 3D box. This pipeline is tested on the KITTI dataset using two different 2D detection networks.  ...  Much of the current methods support 2D vehicle detection.  ...  Subsequently, a set of 3D points which fall into the 2D bounding box after projection is selected. With this set, a model fitting algorithm detects the 3D location and 3D bounding box of the vehicle.  ... 
arXiv:1803.00387v1 fatcat:dluburmvkrh75gecpcjfv7wmny

PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points [article]

Siyuan Huang, Yixin Chen, Tao Yuan, Siyuan Qi, Yixin Zhu, Song-Chun Zhu
2019 arXiv   pre-print
We further devise PerspectiveNet, an end-to-end trainable model that simultaneously detects the 2D bounding box, 2D perspective points, and 3D object bounding box for each object from a single RGB image  ...  PerspectiveNet yields three unique advantages: (i) 3D object bounding boxes are estimated based on perspective points, bridging the gap between 2D and 3D bounding boxes without the need of category-specific  ...  deep learning methods that directly estimates the 3D object bounding boxes from 2D bounding boxes [54, 34–36].  ... 
arXiv:1912.07744v1 fatcat:6v6rh2uiunfwhbsokfnbcws43m

Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview [article]

Zhaoxin Fan, Yazhi Zhu, Yulin He, Qi Sun, Hongyan Liu, Jun He
2022 arXiv   pre-print
Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others.  ...  Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.  ...  Specifically, given a series of images, it first detects 2D bounding boxes of objects to generate proposals for each image.  ... 
arXiv:2105.14291v2 fatcat:2kxd4owthvf7tbcbnlqlqu4r3m

3D Object Proposals using Stereo Imagery for Accurate Object Class Detection [article]

Xiaozhi Chen and Kaustav Kundu and Yukun Zhu and Huimin Ma and Sanja Fidler and Raquel Urtasun
2017 arXiv   pre-print
In particular, we employ a convolutional neural net (CNN) that exploits context and depth information to jointly regress to 3D bounding box coordinates and object pose.  ...  We then exploit a CNN on top of these proposals to perform object detection.  ...  We propose a 3D object proposal method that goes beyond 2D bounding boxes and is capable of generating highquality 3D bounding box proposals.  ... 
arXiv:1608.07711v2 fatcat:3xhhpw2o7nggnjfmf24w5wy6ty
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