Multi-View PointNet for 3D Scene Understanding

Jaritz, Maximilian; Gu, Jiwei; Su, Hao

doi:10.1109/iccvw.2019.00494

Cited by 154 publications

(73 citation statements)

References 32 publications

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“…Representation Learning on Point Clouds. Recently representation learning on point clouds has drawn lots of attention for improving the performance of point cloud classification and segmentation [10], [18], [19], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79]. In terms of 3D detection, previous methods generally project the point clouds to regular bird view grids [9], [12] or 3D voxels [10], [80] for processing point clouds with 2D/3D CNN.…”

Section: D Object Detection With Point Cloudsmentioning

confidence: 99%

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)

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%

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)

1,580

1,045

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“…PointCNN [25], PointConv [53]) by 2-10% in the mean IoU evaluations. This is because [6] 41.72 64.62 PVConv [27] 52.3 -TangentConv [44] 52.80 60.70 3D RNN [59] 53.40 71.30 PointCNN [25] 57.26 63.86 SuperpointGraph [22] 58.04 66.50 MinkNet32 [3] 65.35 71.71 KPConv [46] 67 [37] 33.9 TangetConv [44] 43.8 SurfaceConv [32] 44.2 MVPNet [16] 64.1 PointConv [53] 66.6 PointASNL [56] 66.6 MinkNet42 (5cm) [3] 67.9 KPConv [46] 68.4 FusionNet (5cm)…”

Section: ) 3dsis and Scannetmentioning

confidence: 99%

Deep FusionNet for Point Cloud Semantic Segmentation

Zhang

Jin

Wah

et al. 2020

Lecture Notes in Computer Science

130

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Many point cloud segmentation methods rely on transferring irregular points into a voxel-based regular representation. Although voxel-based convolutions are useful for feature aggregation, they produce ambiguous or wrong predictions if a voxel contains points from different classes. Other approaches (such as PointNets and point-wise convolutions) can take irregular points for feature learning. But their high memory and computational costs (such as for neighborhood search and ball-querying) limit their ability and accuracy for large-scale point cloud processing. To address these issues, we propose a deep fusion network architecture (FusionNet) with a unique voxel-based "mini-PointNet" point cloud representation and a new feature aggregation module (fusion module) for large-scale 3D semantic segmentation. Our FusionNet can learn more accurate point-wise predictions when compared to voxel-based convolutional networks. It can realize more effective feature aggregations with lower memory and computational complexity for large-scale point cloud segmentation when compared to the popular point-wise convolutions. Our experimental results show that FusionNet can take more than one million points on one GPU for training to achieve state-of-the-art accuracy on large-scale Semantic KITTI benchmark. The code will be available at https://github.com/feihuzhang/LiDARSeg.

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“…Extending the same idea and to capture the contextual information of local patterns inside point clouds, PointNet++ [7] applies sampling and grouping operations to extract features from point clusters hierarchically. In recent years, many networks for 3D point clouds were inspired by PointNet++ [7], such as [8][9][10]. A complete review of deep learning methods for point clouds can be found in [11].…”

Section: Point Cloudsmentioning

confidence: 99%

“…PointNet [6] and PointNet++ [7] have made fundamental improvements in this case to train a model directly through the 3D point clouds. Recently, many network architectures [8][9][10][11] were inspired by these pioneer techniques [6,7]. However, applying CNNs for the edge detection problem in point clouds is still challenging.…”

Section: Introductionmentioning

confidence: 99%

EDC-Net: Edge Detection Capsule Network for 3D Point Clouds

Bazazian

Parés

2021

Applied Sciences

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Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.

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Multi-View PointNet for 3D Scene Understanding

Cited by 154 publications

References 32 publications

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

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

Deep FusionNet for Point Cloud Semantic Segmentation

EDC-Net: Edge Detection Capsule Network for 3D Point Clouds

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