GridPointNet: Grid and Point-Based 3D Object Detection from Point Cloud

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.

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

10.1109/ijcnn52387.2021.9534412 ◽

2021 ◽

Author(s):

Xinrui Yan ◽

Yuhao Huang ◽

Shitao Chen ◽

Zhixiong Nan ◽

Jingmin Xin ◽

...

Keyword(s):

Object Detection ◽

Point Cloud ◽

3D Object ◽

3D Object Detection

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

Sensors ◽

10.3390/s19194093 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4093 ◽

Cited By ~ 7

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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PVF-NET: Point & Voxel Fusion 3D Object Detection Framework for Point Cloud

2020 17th Conference on Computer and Robot Vision (CRV) ◽

10.1109/crv50864.2020.00025 ◽

2020 ◽

Author(s):

Zhihao Cui ◽

Zhenhua Zhang

Keyword(s):

Object Detection ◽

Point Cloud ◽

3D Object ◽

3D Object Detection

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From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/tpami.2020.2977026 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 19

Author(s):

Shaoshuai Shi ◽

Zhe Wang ◽

Jianping Shi ◽

Xiaogang Wang ◽

Hongsheng Li

Keyword(s):

Object Detection ◽

Point Cloud ◽

3D Object ◽

3D Object Detection

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Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) ◽

10.1109/iccvw.2019.00114 ◽

2019 ◽

Cited By ~ 23

Author(s):

Xinshuo Weng ◽

Kris Kitani

Keyword(s):

Object Detection ◽

Point Cloud ◽

3D Object ◽

3D Object Detection

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

10.36227/techrxiv.12593555.v1 ◽

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.

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

Sensors ◽

10.3390/s20216043 ◽

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.

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