P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection From Point Clouds

Li, Jiale; Sun, Yu; Luo, Shujie; Zhu, Ziqi; Dai, Hang; Krylov, Andrey S.; Ding, Yong; Shao, Ling

doi:10.1109/access.2021.3094562

Cited by 26 publications

(11 citation statements)

References 48 publications

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“…Authors in [40], proposed regional-based convolutional network (RCNN) to detect 3D objects. Voxalization, feature extraction, and 3D box generation were performed to detect the 3D objects.…”

Section: Problem Statementmentioning

confidence: 99%

“…This section provides the comparative analysis of the proposed 3D-YOLOv4 in which the proposed work is compared with existing methods such as P2V-CNN [40], Hough-3D [38], and RANSAC [39]. The performance of the proposed work is compared with several metrics such as accuracy (%), precision (%), recall (%), f-measure (%), computational time (ms), and ROC-AUC curve.…”

Section: Comparative Analysismentioning

confidence: 99%

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YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud & RGB-D Data

Rajendran

B.S.

2022

Preprint

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3D object detection is a computer vision task that is used for various applications. However, it has several drawbacks due to occlusion, noise, inefficient field of view, etc. In addition, the recent LiDAR point clouds-based 3D object detection methods lack sparsity and homogeneity which affects the detection accuracy. In this paper, we detect 3D objects based on static and dynamic states by performing preprocessing, segmentation, extraction of features, classification, and regression considering RGB-D images, and LiDAR point clouds. Firstly, the quality of the image is enhanced by performing preprocessing which consists of two levels. In the first level, noise removal is performed for both RGB-D images and LiDAR cloud points by implementing Thresholding based Adaptive Median Filtering (TAMF) algorithm. In the second level, we perform point to voxel conversion for the LiDAR point clouds and fuse both RGB-D images and 3D LiDAR point clouds for better segmentation. Secondly, object segmentation is performed using Improved Mask R-CNN algorithm which creates masked instant bounding boxes by performing semantic segmentation considering both point and local-based information. Estimating pose for the segmented objects using bearing angle-based affine transformation method to extract features efficiently. Thirdly, Feature extraction is performed from the segmented 3D objects by considering high and low-level features using Attention-based YOLO V4 Tiny network and fusing both features for reducing the feature redundancy. Finally, based on the extracted feature the objects are classified as static and dynamic, and 3D bounding boxes are generated to improve the 3D object detection accuracy. The proposed work is evaluated by KITTI dataset. MATLAB R2020a simulation tool is used to perform simulation for the proposed work. The performance of the 3D object detection is evaluated in terms of several performance metrics such as accuracy, precision, recall, F-measure, computation time, and ROC-AUC curve compared to existing methods.

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Section: Problem Statementmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud & RGB-D Data

Rajendran

B.S.

2022

Preprint

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“…Voxel-Point based 3D Object Detection. The voxel-point based methods [18,33,47] use both representations. PointsPool [47] voxelizes the point cloud around the object proposal to encode the empty and non-empty regions for compact proposal-wise feature learning.…”

Section: Related Workmentioning

confidence: 99%

From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder

Dai

Shao

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder. To preserve the necessary information from all raw points and maintain the high box recall in voxel based Region Proposal Network (RPN), we propose a residual voxel-to-point decoder to extract the point features in addition to the map-view features from the voxel based RPN. We use a 3D Region of Interest (RoI) alignment to crop and align the features with the proposal boxes for accurately perceiving the object position. The RoI-Aligned features are finally aggregated with the corner geometry embeddings that can provide the potentially missing corner information in the box refinement stage. We propose a simple and efficient method to align the estimated IoUs to the refined proposal boxes as a more relevant localization confidence. The comprehensive experiments on KITTI and Waymo Open Dataset demonstrate that our method achieves significant improvements with novel architectures against the existing methods. The code is available on Github URL 1 . CCS CONCEPTS• Computing methodologies → Object detection; Scene understanding; Vision for robotics.

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“…VoxelNet [67] evenly divides a point cloud into small 3D voxels in XYZ directions for 3D convolutional processing. PointPillars [21] roughly generate pillars along the height direction and the following works [24,30,62] develop better strategies for expressive feature learning. Considering that most of the voxels are empty, SECOND [58] introduces the sparse 3D convolution [12,13] for efficient voxel processing.…”

Section: Related Workmentioning

confidence: 99%

Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud

Dai

Shao

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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Most of the existing single-stage and two-stage 3D object detectors are anchor-based methods, while the efficient but challenging anchor-free single-stage 3D object detection is not well investigated. Recent studies on 2D object detection show that the anchor-free methods also are of great potential. However, the unordered and sparse properties of point clouds prevent us from directly leveraging the advanced 2D methods on 3D point clouds. We overcome this by converting the voxel-based sparse 3D feature volumes into the sparse 2D feature maps. We propose an attentive module to fit the sparse feature maps to dense mostly on the object regions through the deformable convolution tower and the supervised mask-guided attention. By directly regressing the 3D bounding box from the enhanced and dense feature maps, we construct a novel singlestage 3D detector for point clouds in an anchor-free manner. We propose an IoU-based detection confidence re-calibration scheme to improve the correlation between the detection confidence score and the accuracy of the bounding box regression. Our code is publicly available at https://github.com/jialeli1/MGAF-3DSSD. CCS CONCEPTS• Computing methodologies → Object detection; Scene understanding; Vision for robotics.

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P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection From Point Clouds

Cited by 26 publications

References 48 publications

YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud & RGB-D Data

YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud & RGB-D Data

From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder

Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud

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P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection From Point Clouds

Cited by 26 publications

References 48 publications

YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud &amp; RGB-D Data

YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud &amp; RGB-D Data

From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder

Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud

Contact Info

Product

Resources

About

YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud & RGB-D Data

YOLOv4Tiny: Bearing Angle Based Pose Estimation and Semantic Segmentation For 3D Object Detection From LiDAR Point Cloud & RGB-D Data