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

Shi, Shaoshuai; Wang, Zhe; Shi, Jianping; Wang, Xiaogang; Li, Hongsheng

doi:10.1109/tpami.2020.2977026

Cited by 589 publications

(406 citation statements)

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“…Some other works [8], [10] divide the point clouds into 3D voxels to be processed by 3D CNN, and 3D sparse convolution [61] is introduced [16] for efficient 3D voxel processing. [62], [63] utilize multiple detection heads while [24] explores the object part locations for improving the performance. In addition, [64], [65] predicts bounding box parameters following the anchor-free paradigm.…”

Section: D Object Detection With Point Cloudsmentioning

confidence: 99%

“…For the sake of high-quality 3D box prediction, we observe that, the voxel-based representation with intensive predefined anchors could generate better 3D box proposals with higher recall rate [24], while performing the proposal refinement on the 3D point-wise features could naturally benefit from more fine-grained point locations. Motivated by these observations, we argue that the performance of 3D object detection can be boosted by intertwining diverse local feature learning strategies from both voxel-based and pointbased representations.…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

Shi

Guo²,

Jiang³

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

1,580

1,045

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3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields. In this paper, we propose the Point-Voxel Region based Convolution Neural Networks (PV-RCNNs) for accurate 3D detection from point clouds. First, we propose a novel 3D object detector, PV-RCNN-v1, which employs the voxel-to-keypoint scene encoding and keypoint-to-grid RoI feature abstraction two novel steps. These two steps deeply incorporate both 3D voxel CNN and PointNet-based set abstraction for learning discriminative point-cloud features. Second, we propose a more advanced framework, PV-RCNN-v2, for more efficient and accurate 3D detection. It consists of two major improvements, where the first one is the sectorized proposal-centric strategy for efficiently producing more representative and uniformly distributed keypoints, and the second one is the VectorPool aggregation to replace set abstraction for better aggregating local point-cloud features with much less resource consumption. With these two major modifications, our PV-RCNN-v2 runs more than twice as fast as the v1 version while still achieving better performance on the large-scale Waymo Open Dataset with 150m × 150m detection range. Extensive experiments demonstrate that our proposed PV-RCNNs significantly outperform previous state-of-the-art 3D detection methods on both the Waymo Open Dataset and the highly-competitive KITTI benchmark.

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Section: D Object Detection With Point Cloudsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

Shi

Guo²,

Jiang³

et al. 2020

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

1,580

1,045

View full text Add to dashboard Cite

show abstract

“…In [224], the "Second" network was designed based on "Voxelnet" to improve the processing capacity for sparse voxel grid, and a "angle loss regression equation" was designed to improve the detection performance of attitude angle. Subsequent improvements based on "Voxelnet" include "Part-A2" in [225] and "MVX-NET" in [226].…”

Section: Voxel Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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“…With the emerging technique development of deep learning 17 such as convolution neural networks (CNN), there are significant performance improvements for different tasks in the domain of computer vision, for example, image classification, 18,19 object detection, 20,21 and image segmentation. [22][23][24] Different from traditional machine learning methods [25][26][27][28] applied in different domains, [29][30][31]…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

Reconstruction enhanced probabilistic model for semisupervised tongue image segmentation

Zhou

Fan

Zhao

et al. 2020

Concurrency and Computation

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Tongue segmentation is a key step of automatic tongue diagnosis, and the major challenges for the effective segmentation lie in the large appearance variations of tongue caused by different diseases, for example, tongue coating and tongue texture. Moreover, the limited labeled data also hinders traditional supervised methods from their powerful learning ability. To alleviate these challenges, in this work, we propose a reconstruction enhanced probabilistic model for semisupervised tongue segmentation, named SemiTongue, in which, image reconstruction constraint combined with adversarial learning is used to improve the accuracy of tongue segmentation. Specifically, based on a shared feature encoder that served as an inference model, two separate branches in SemiTongue as the generative model, which are composed of a segmentation decoder and a reconstruction decoder, are utilized to generate the tongue segmentation and reconstruct original tongue image respectively. Then, a discriminator is employed to differentiate the generated segmentation map from the ground truth segmentation distribution. Moreover, semisupervised learning is conducted through discriminator by discovering the reliable region in the generated segmentation map of unlabeled images, which is further utilized to supervise the segmentation branch. Experimental results compared with state-of-the-art methods on real-world datasets demonstrate the effectiveness of SemiTongue.

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

Cited by 589 publications

References 50 publications

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Reconstruction enhanced probabilistic model for semisupervised tongue image segmentation

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