scholarly journals TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

2020 ◽  
Vol 34 (07) ◽  
pp. 11677-11684 ◽  
Author(s):  
Zhe Liu ◽  
Xin Zhao ◽  
Tengteng Huang ◽  
Ruolan Hu ◽  
Yu Zhou ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Detection Accuracy ◽  
The Novel ◽  
3D Object ◽  
Additional Noise ◽  
Crucial Information ◽  
Cloud Points ◽  
Validation Set ◽  
3D Object Detection

In this paper, we focus on exploring the robustness of the 3D object detection in point clouds, which has been rarely discussed in existing approaches. We observe two crucial phenomena: 1) the detection accuracy of the hard objects, e.g., Pedestrians, is unsatisfactory, 2) when adding additional noise points, the performance of existing approaches decreases rapidly. To alleviate these problems, a novel TANet is introduced in this paper, which mainly contains a Triple Attention (TA) module, and a Coarse-to-Fine Regression (CFR) module. By considering the channel-wise, point-wise and voxel-wise attention jointly, the TA module enhances the crucial information of the target while suppresses the unstable cloud points. Besides, the novel stacked TA further exploits the multi-level feature attention. In addition, the CFR module boosts the accuracy of localization without excessive computation cost. Experimental results on the validation set of KITTI dataset demonstrate that, in the challenging noisy cases, i.e., adding additional random noisy points around each object, the presented approach goes far beyond state-of-the-art approaches. Furthermore, for the 3D object detection task of the KITTI benchmark, our approach ranks the first place on Pedestrian class, by using the point clouds as the only input. The running speed is around 29 frames per second.

scholarly journals Optimization of the PointPillars network for 3D object detection in point clouds

2020 ◽  
Author(s):  
Joanna Stanisz ◽  
Konrad Lis ◽  
Tomasz Kryjak ◽  
Marek Gorgon
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Main Part ◽  
Point Clouds ◽  
Lidar Data ◽  
Detection Accuracy ◽  
3D Object ◽  
Fold Reduction ◽  
Low Energy Consumption ◽  
3D Object Detection

In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. Techniques like quantisation and pruning available in the Brevitas and PyTorch tools were used. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. The aim of this work was to propose a variant of the network which we will ultimately implement in an FPGA device. This will allow for real-time LiDAR data processing with low energy consumption. The obtained results indicate that even a significant quantisation from 32-bit floating point to 2-bit integer in the main part of the algorithm, results in 5%-9% decrease of the detection accuracy, while allowing for almost a 16-fold reduction in size of the model.

scholarly journals Optimization of the PointPillars network for 3D object detection in point clouds

2020 ◽  
Author(s):  
Joanna Stanisz ◽  
Konrad Lis ◽  
Tomasz Kryjak ◽  
Marek Gorgon
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Main Part ◽  
Point Clouds ◽  
Lidar Data ◽  
Detection Accuracy ◽  
3D Object ◽  
Fold Reduction ◽  
Low Energy Consumption ◽  
3D Object Detection

In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. Techniques like quantisation and pruning available in the Brevitas and PyTorch tools were used. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. The aim of this work was to propose a variant of the network which we will ultimately implement in an FPGA device. This will allow for real-time LiDAR data processing with low energy consumption. The obtained results indicate that even a significant quantisation from 32-bit floating point to 2-bit integer in the main part of the algorithm, results in 5%-9% decrease of the detection accuracy, while allowing for almost a 16-fold reduction in size of the model.

scholarly journals Learning Deformable Network for 3D Object Detection on Point Clouds

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wanyi Zhang ◽  
Xiuhua Fu ◽  
Wei Li
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Three Dimensional ◽  
Ground Truth ◽  
Point Clouds ◽  
Detection Accuracy ◽  
3D Object ◽  
Cloud Data ◽  
Dimensional Object ◽  
3D Object Detection

3D object detection based on point cloud data in the unmanned driving scene has always been a research hotspot in unmanned driving sensing technology. With the development and maturity of deep neural networks technology, the method of using neural network to detect three-dimensional object target begins to show great advantages. The experimental results show that the mismatch between anchor and training samples would affect the detection accuracy, but it has not been well solved. The contributions of this paper are as follows. For the first time, deformable convolution is introduced into the point cloud object detection network, which enhances the adaptability of the network to vehicles with different directions and shapes. Secondly, a new generation method of anchor in RPN is proposed, which can effectively prevent the mismatching between the anchor and ground truth and remove the angle classification loss in the loss function. Compared with the state-of-the-art method, the AP and AOS of the detection results are improved.

scholarly journals Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1205
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Alignment Algorithm ◽  
3D Object ◽  
3D Point Clouds ◽  
Object Feature ◽  
3D Object Detection ◽  
Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

Adversarial Training on Point Clouds for Sim-to-Real 3D Object Detection

2021 ◽  
pp. 1-1
Author(s):  
Robert Debortoli ◽  
Fuxin Li ◽  
Ashish Kapoor ◽  
Geoffrey Hollinger
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
3D Object ◽  
Adversarial Training ◽  
3D Object Detection

Generating Adversarial Point Clouds on Multi-modal Fusion Based 3D Object Detection Model

2021 ◽  
pp. 187-203
Author(s):  
Huiying Wang ◽  
Huixin Shen ◽  
Boyang Zhang ◽  
Yu Wen ◽  
Dan Meng
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
3D Object ◽  
Detection Model ◽  
3D Object Detection

scholarly journals 3D Object Detection Using Scale Invariant and Feature Reweighting Networks

2019 ◽  
Vol 33 ◽  
pp. 9267-9274 ◽  
Author(s):  
Xin Zhao ◽  
Zhe Liu ◽  
Ruolan Hu ◽  
Kaiqi Huang
Keyword(s):  
Object Detection ◽  
Network Architecture ◽  
Point Clouds ◽  
Scale Invariant ◽  
3D Object ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
Bounding Boxes ◽  
The One ◽  
3D Object Detection

3D object detection plays an important role in a large number of real-world applications. It requires us to estimate the localizations and the orientations of 3D objects in real scenes. In this paper, we present a new network architecture which focuses on utilizing the front view images and frustum point clouds to generate 3D detection results. On the one hand, a PointSIFT module is utilized to improve the performance of 3D segmentation. It can capture the information from different orientations in space and the robustness to different scale shapes. On the other hand, our network obtains the useful features and suppresses the features with less information by a SENet module. This module reweights channel features and estimates the 3D bounding boxes more effectively. Our method is evaluated on both KITTI dataset for outdoor scenes and SUN-RGBD dataset for indoor scenes. The experimental results illustrate that our method achieves better performance than the state-of-the-art methods especially when point clouds are highly sparse.

scholarly journals Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 10478-10485 ◽  
Author(s):  
Yingjie Cai ◽  
Buyu Li ◽  
Zeyu Jiao ◽  
Hongsheng Li ◽  
Xingyu Zeng ◽  
...  
Keyword(s):  
Object Detection ◽  
Depth Estimation ◽  
Physical World ◽  
Depth Information ◽  
Detection Accuracy ◽  
Unified Framework ◽  
3D Object ◽  
Depth Recovery ◽  
Bounding Boxes ◽  
3D Object Detection

Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images. Since the location recovery in 3D space is quite difficult on account of absence of depth information, this paper proposes a novel unified framework which decomposes the detection problem into a structured polygon prediction task and a depth recovery task. Different from the widely studied 2D bounding boxes, the proposed novel structured polygon in the 2D image consists of several projected surfaces of the target object. Compared to the widely-used 3D bounding box proposals, it is shown to be a better representation for 3D detection. In order to inversely project the predicted 2D structured polygon to a cuboid in the 3D physical world, the following depth recovery task uses the object height prior to complete the inverse projection transformation with the given camera projection matrix. Moreover, a fine-grained 3D box refinement scheme is proposed to further rectify the 3D detection results. Experiments are conducted on the challenging KITTI benchmark, in which our method achieves state-of-the-art detection accuracy.

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