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PLUMENet: Efficient 3D Object Detection from Stereo Images [article]

Yan Wang, Bin Yang, Rui Hu, Ming Liang, Raquel Urtasun
2021 arXiv   pre-print
Specifically, we directly construct a pseudo LiDAR feature volume (PLUME) in 3D space, which is then used to solve both depth estimation and object detection tasks.  ...  3D object detection is a key component of many robotic applications such as self-driving vehicles.  ...  To overcome these issues, we propose an efficient stereo-based detector by replacing the disparity cost volume in image space with a novel pseudo LiDAR feature volume (PLUME) in 3D space.  ... 
arXiv:2101.06594v3 fatcat:spdwihruxney5omg23w54gszky

RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement [article]

Kiwoo Shin, Youngwook Paul Kwon, Masayoshi Tomizuka
2018 arXiv   pre-print
Inspired by PointNet, RoarNet_3D processes 3D point clouds directly without any loss of data, leading to precise detection. We evaluate our method in KITTI, a 3D object detection benchmark.  ...  We present RoarNet, a new approach for 3D object detection from a 2D image and 3D Lidar point clouds.  ...  ACKNOWLEDGMENT The work was in part supported by Berkeley Deep Drive. Kiwoo Shin is supported by Samsung Scholarship.  ... 
arXiv:1811.03818v1 fatcat:ftjcbkgihbea7aiv22aanscb7y

ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection [article]

Aqi Gao, Yanwei Pang, Jing Nie, Jiale Cao, Yishun Guo
2022 arXiv   pre-print
We hope that our efficient stereo geometry network can provide more possible directions for fast 3D object detection. Our source code will be released.  ...  We argue that the main reason is due to the poor geometry-aware feature representation in 3D space. To solve this problem, we propose an efficient stereo geometry network (ESGN).  ...  Prediction Heads 3D Detection Head Similar to [3, 5] , we perform 3D detection by a classification head and a regression head.  ... 
arXiv:2111.14055v2 fatcat:zculgzbobzakxkll4baabgmfmm

Complex-YOLO: Real-time 3D Object Detection on Point Clouds [article]

Martin Simon, Stefan Milz, Karl Amende, Horst-Michael Gross
2018 arXiv   pre-print
Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency.  ...  In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space  ...  Conclusion In this paper we present the first real-time efficient deep learning model for 3D object detection on Lidar based point clouds.  ... 
arXiv:1803.06199v2 fatcat:ad7qzlclnvas7mtqs537khjqr4

Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds [chapter]

Martin Simon, Stefan Milz, Karl Amende, Horst-Michael Gross
2019 Lecture Notes in Computer Science  
Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency.  ...  In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space  ...  Conclusion In this paper we present the first real-time efficient deep learning model for 3D object detection on Lidar based point clouds.  ... 
doi:10.1007/978-3-030-11009-3_11 fatcat:seo5lofl7fgabdmyupjuwatyzu

Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve [article]

Weicheng Kuo, Anelia Angelova, Tsung-Yi Lin, Angela Dai
2020 arXiv   pre-print
We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their  ...  We construct a joint embedding space between the detected regions of an image corresponding to an object and 3D CAD models, enabling retrieval of CAD models for an input RGB image.  ...  By leveraging CAD models as shape representation, we are able to predict multiple distinct 3D objects per image efficiently (approximately 60ms per image).  ... 
arXiv:2007.13034v1 fatcat:yabpz6uoczdhtopk2fmmfkwqq4

F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking [article]

Hao Zou, Jinhao Cui, Xin Kong, Chujuan Zhang, Yong Liu, Feng Wen, Wanlong Li
2020 arXiv   pre-print
A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates.  ...  For efficiency, our approach gains better performance with fewer candidates by reducing search space.  ...  F-PointNet [10] uses 2D detection result to generate frustums in 3D space, which greatly reduces the search space.  ... 
arXiv:2010.11510v1 fatcat:a4na7ek475enzjll64biqucydi

RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving [article]

Peixuan Li, Shun Su, Huaici Zhao
2020 arXiv   pre-print
In this paper, we propose an efficient and accurate 3D object detection method from stereo images, named RTS3D.  ...  Although the recent image-based 3D object detection methods using Pseudo-LiDAR representation have shown great capabilities, a notable gap in efficiency and accuracy still exist compared with LiDAR-based  ...  An image-based 3D object detection approach predicts the 3D box of objects more efficiently and accurately. 2.)  ... 
arXiv:2012.15072v1 fatcat:yp4v3vhcjvft5f6agkxydeuure

Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection [article]

Aqi Gao, Jiale Cao, Yanwei Pang
2021 arXiv   pre-print
FCE space splits the entire object region into 3D uniform grid latent space for feature sampling point generation, which ignores the importance of different object regions.  ...  To solve these two issues, a recently introduced RTS3D builds an efficient 4D Feature-Consistency Embedding (FCE) space for the intermediate representation of object without depth supervision.  ...  C. 3D detection As mentioned above, each 3D proposal is represented by the 4D feature in FCE space. Next, we use a 3D object detector to perform 3D detection.  ... 
arXiv:2106.10013v3 fatcat:sa4hbl7nufguxaqj46flhl4dje

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving [article]

Peixuan Li, Huaici Zhao, Pengfei Liu, Feidao Cao
2020 arXiv   pre-print
In this work, we propose an efficient and accurate monocular 3D detection framework in single shot.  ...  the dimension, location, and orientation in 3D space.  ...  Figure 1 . 1 Overview of proposed method: We first predict ordinal keypoints projected in the image space by eight vertexes and a central point of a 3D object.  ... 
arXiv:2001.03343v1 fatcat:v34dca7k4vat3hp57yjfzt5d5y

3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images

Xiaoke Shen, Ioannis Stamos
2021 Sensors  
We address those problems by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, depth only, or RGB-D images.  ...  to perform the 3D instance segmentation and object detection.  ...  Given RGB-D data or depth only data as input, 3D object detection aims to classify and localize objects in 3D space.  ... 
doi:10.3390/s21041213 pmid:33572289 fatcat:3rznt7afljg3lflvlcdqyccwgm

MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time [article]

Xichuan Zhou, Yicong Peng, Chunqiao Long, Fengbo Ren, Cong Shi
2020 arXiv   pre-print
Monocular multi-object detection and localization in 3D space has been proven to be a challenging task.  ...  Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25\% and 94.74\%, respectively.  ...  Acknowledgements This work was supported by the National Natural Science Foundation of China under Contract 61971072.  ... 
arXiv:2006.16007v1 fatcat:icbn75qefjfxxl777ahs7vyf4e

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
in the 3D space.  ...  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image.  ...  The goal is to predict a tight bounding-box with a high 3D-IoU. Fig. 2 . The proposed framework for monocular 3D object detection. It is an iterative algorithm optimized by RL.  ... 
arXiv:2008.13748v1 fatcat:5eorvtrt3fgtzmyhetqkxmbr4a

Frustum VoxNet for 3D object detection from RGB-D or Depth images [article]

Xiaoke Shen, Ioannis Stamos
2020 arXiv   pre-print
In this work, we describe a new 3D object detection system from an RGB-D or depth-only point cloud. Our system first detects objects in 2D (either RGB or pseudo-RGB constructed from depth).  ...  The next step is to detect 3D objects within the 3D frustums these 2D detections define.  ...  inspired by 2D-driven 3D object detection approaches as in [10, 16] .  ... 
arXiv:1910.05483v2 fatcat:q4fdzf4albeb5n6j2i7xvsybyu

Frustum PointNets for 3D Object Detection from RGB-D Data

Charles R. Qi, Wei Liu, Chenxia Wu, Hao Su, Leonidas J. Guibas
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.  ...  However, a key challenge of this approach is how to efficiently localize objects in point clouds of large-scale scenes (region proposal).  ...  Towards this goal, we have to address one key challenge: how to efficiently propose possible locations of 3D objects in a 3D space.  ... 
doi:10.1109/cvpr.2018.00102 dblp:conf/cvpr/QiLWSG18 fatcat:za5o64qpcrgqvijmioux6clnpq
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