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 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 EPGNet: Enhanced Point Cloud Generation for 3D Object Detection

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6927
Author(s):  
Qingsheng Chen ◽  
Cien Fan ◽  
Weizheng Jin ◽  
Lian Zou ◽  
Fangyu Li ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Cloud Data ◽  
Second Stage ◽  
Real Scene ◽  
Previous State ◽  
Dimensional Object ◽  
3D Object Detection

Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder–decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird’s eye view) object detection compared with some previous state-of-the-art methods.

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.

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.

scholarly journals 3D-GIoU: 3D Generalized Intersection over Union for Object Detection in Point Cloud

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4093 ◽  
Author(s):  
Jun Xu ◽  
Yanxin Ma ◽  
Songhua He ◽  
Jiahua Zhu
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Pedestrian Detection ◽  
Three Dimensional ◽  
Average Precision ◽  
3D Object ◽  
Automatic Driving ◽  
3D Computer Vision ◽  
High Level ◽  
3D Object Detection

Three-dimensional (3D) object detection is an important research in 3D computer vision with significant applications in many fields, such as automatic driving, robotics, and human–computer interaction. However, the low precision is an urgent problem in the field of 3D object detection. To solve it, we present a framework for 3D object detection in point cloud. To be specific, a designed Backbone Network is used to make fusion of low-level features and high-level features, which makes full use of various information advantages. Moreover, the two-dimensional (2D) Generalized Intersection over Union is extended to 3D use as part of the loss function in our framework. Empirical experiments of Car, Cyclist, and Pedestrian detection have been conducted respectively on the KITTI benchmark. Experimental results with average precision (AP) have shown the effectiveness of the proposed network.

scholarly journals A Two-Phase Cross-Modality Fusion Network for Robust 3D Object Detection

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6043
Author(s):  
Yujun Jiao ◽  
Zhishuai Yin
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Point Clouds ◽  
Second Phase ◽  
Two Phase ◽  
3D Object ◽  
Rgb Images ◽  
Fusion Scheme ◽  
3D Object Detection ◽  
Level Fusion

A two-phase cross-modality fusion detector is proposed in this study for robust and high-precision 3D object detection with RGB images and LiDAR point clouds. First, a two-stream fusion network is built into the framework of Faster RCNN to perform accurate and robust 2D detection. The visible stream takes the RGB images as inputs, while the intensity stream is fed with the intensity maps which are generated by projecting the reflection intensity of point clouds to the front view. A multi-layer feature-level fusion scheme is designed to merge multi-modal features across multiple layers in order to enhance the expressiveness and robustness of the produced features upon which region proposals are generated. Second, a decision-level fusion is implemented by projecting 2D proposals to the space of the point cloud to generate 3D frustums, on the basis of which the second-phase 3D detector is built to accomplish instance segmentation and 3D-box regression on the filtered point cloud. The results on the KITTI benchmark show that features extracted from RGB images and intensity maps complement each other, and our proposed detector achieves state-of-the-art performance on 3D object detection with a substantially lower running time as compared to available competitors.

scholarly journals Voxel-FPN: Multi-Scale Voxel Feature Aggregation for 3D Object Detection from LIDAR Point Clouds

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 704 ◽  
Author(s):  
Hongwu Kuang ◽  
Bei Wang ◽  
Jianping An ◽  
Ming Zhang ◽  
Zehan Zhang
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Maps ◽  
3D Object ◽  
Cloud Data ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Point Data ◽  
3D Object Detection

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-Feature Pyramid Network, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts and fuses multi-scale voxel information in a bottom-up manner, whereas decoder fuses multiple feature maps from various scales by Feature Pyramid Network in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios.

scholarly journals Accurate 3D Object Detection from Point Cloud Data using Bird’s Eye View Representations

2021 ◽  
Author(s):  
Nerea Aranjuelo ◽  
Guus Engels ◽  
David Montero ◽  
Marcos Nieto ◽  
Ignacio Arganda-Carreras ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Point Cloud Data ◽  
3D Object ◽  
Cloud Data ◽  
3D Object Detection

scholarly journals Unsupervised Subcategory Domain Adaptive Network for 3D Object Detection in LiDAR

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 927
Author(s):  
Zhiyu Wang ◽  
Li Wang ◽  
Liang Xiao ◽  
Bin Dai
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Three Dimensional ◽  
Autonomous Driving ◽  
Adaptive Method ◽  
Target Domain ◽  
Adaptive Network ◽  
General Object ◽  
Dimensional Object ◽  
3D Object Detection

Three-dimensional object detection based on the LiDAR point cloud plays an important role in autonomous driving. The point cloud distribution of the object varies greatly at different distances, observation angles, and occlusion levels. Besides, different types of LiDARs have different settings of projection angles, thus producing an entirely different point cloud distribution. Pre-trained models on the dataset with annotations may degrade on other datasets. In this paper, we propose a method for object detection using an unsupervised adaptive network, which does not require additional annotation data of the target domain. Our object detection adaptive network consists of a general object detection network, a global feature adaptation network, and a special subcategory instance adaptation network. We divide the source domain data into different subcategories and use a multi-label discriminator to assign labels dynamically to the target domain data. We evaluated our approach on the KITTI object benchmark and proved that the proposed unsupervised adaptive method could achieve a remarkable improvement in the adaptation capabilities.

scholarly journals 3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds

Signals ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 98-107
Author(s):  
Yiran Li ◽  
Han Xie ◽  
Hyunchul Shin
Keyword(s):  
Object Detection ◽  
Three Dimensional ◽  
Point Clouds ◽  
Practical Method ◽  
Autonomous Driving ◽  
Feature Maps ◽  
3D Object ◽  
On The Road ◽  
3D Object Detection ◽  
Hard Cases

Three-dimensional (3D) object detection is essential in autonomous driving. Three-dimensional (3D) Lidar sensor can capture three-dimensional objects, such as vehicles, cycles, pedestrians, and other objects on the road. Although Lidar can generate point clouds in 3D space, it still lacks the fine resolution of 2D information. Therefore, Lidar and camera fusion has gradually become a practical method for 3D object detection. Previous strategies focused on the extraction of voxel points and the fusion of feature maps. However, the biggest challenge is in extracting enough edge information to detect small objects. To solve this problem, we found that attention modules are beneficial in detecting small objects. In this work, we developed Frustum ConvNet and attention modules for the fusion of images from a camera and point clouds from a Lidar. Multilayer Perceptron (MLP) and tanh activation functions were used in the attention modules. Furthermore, the attention modules were designed on PointNet to perform multilayer edge detection for 3D object detection. Compared with a previous well-known method, Frustum ConvNet, our method achieved competitive results, with an improvement of 0.27%, 0.43%, and 0.36% in Average Precision (AP) for 3D object detection in easy, moderate, and hard cases, respectively, and an improvement of 0.21%, 0.27%, and 0.01% in AP for Bird’s Eye View (BEV) object detection in easy, moderate, and hard cases, respectively, on the KITTI detection benchmarks. Our method also obtained the best results in four cases in AP on the indoor SUN-RGBD dataset for 3D object detection.

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