Robust 3D Object Detection from LiDAR Point Cloud Data with Spatial Information Aggregation

2020 ◽  
pp. 813-823
Author(s):  
Nerea Aranjuelo ◽  
Guus Engels ◽  
Luis Unzueta ◽  
Ignacio Arganda-Carreras ◽  
Marcos Nieto ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Spatial Information ◽  
Information Aggregation ◽  
Point Cloud Data ◽  
3D Object ◽  
Cloud Data ◽  
3D Object Detection

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 Transfer Learning Based Semantic Segmentation for 3D Object Detection from Point Cloud

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3964
Author(s):  
Muhammad Imad ◽  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Point Cloud Data ◽  
Learning Stage ◽  
3D Object ◽  
Cloud Data ◽  
3D Object Detection

Three-dimensional object detection utilizing LiDAR point cloud data is an indispensable part of autonomous driving perception systems. Point cloud-based 3D object detection has been a better replacement for higher accuracy than cameras during nighttime. However, most LiDAR-based 3D object methods work in a supervised manner, which means their state-of-the-art performance relies heavily on a large-scale and well-labeled dataset, while these annotated datasets could be expensive to obtain and only accessible in the limited scenario. Transfer learning is a promising approach to reduce the large-scale training datasets requirement, but existing transfer learning object detectors are primarily for 2D object detection rather than 3D. In this work, we utilize the 3D point cloud data more effectively by representing the birds-eye-view (BEV) scene and propose a transfer learning based point cloud semantic segmentation for 3D object detection. The proposed model minimizes the need for large-scale training datasets and consequently reduces the training time. First, a preprocessing stage filters the raw point cloud data to a BEV map within a specific field of view. Second, the transfer learning stage uses knowledge from the previously learned classification task (with more data for training) and generalizes the semantic segmentation-based 2D object detection task. Finally, 2D detection results from the BEV image have been back-projected into 3D in the postprocessing stage. We verify results on two datasets: the KITTI 3D object detection dataset and the Ouster LiDAR-64 dataset, thus demonstrating that the proposed method is highly competitive in terms of mean average precision (mAP up to 70%) while still running at more than 30 frames per second (FPS).

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 Enhancing Point Features with Spatial Information for Point-Based 3D Object Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huaijin Liu ◽  
Jixiang Du ◽  
Yong Zhang ◽  
Hongbo Zhang
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
Spatial Information ◽  
Structural Information ◽  
Feature Representation ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Long Distance ◽  
3D Object ◽  
3D Object Detection

Currently, there are many kinds of voxel-based multisensor 3D object detectors, while point-based multisensor 3D object detectors have not been fully studied. In this paper, we propose a new 3D two-stage object detection method based on point cloud and image fusion to improve the detection accuracy. To address the problem of insufficient semantic information of point cloud, we perform multiscale deep fusion of LiDAR point and camera image in a point-wise manner to enhance point features. Due to the imbalance of LiDAR points, the object point cloud in the long-distance area is sparse. We design a point cloud completion module to predict the spatial shape of objects in the candidate boxes and extract the structural information to improve the feature representation ability to further refine the boxes. The framework is evaluated on widely used KITTI and SUN-RGBD dataset. Experimental results show that our method outperforms all state-of-the-art point-based 3D object detection methods and has comparable performance to voxel-based methods as well.

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.

DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud

2021 ◽  
Author(s):  
Xinrui Yan ◽  
Yuhao Huang ◽  
Shitao Chen ◽  
Zhixiong Nan ◽  
Jingmin Xin ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
3D Object ◽  
3D Object Detection

scholarly journals Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

2019 ◽  
Vol 12 (1) ◽  
pp. 50 ◽  
Author(s):  
Mahyar Aboutalebi ◽  
Alfonso F. Torres-Rua ◽  
Mac McKee ◽  
William P. Kustas ◽  
Hector Nieto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Point Cloud ◽  
Spatial Information ◽  
Point Cloud Data ◽  
Area Index ◽  
Cloud Data ◽  
Fractional Cover ◽  
Spatially Distributed

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

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.

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