3D Point Cloud Stitching for Object Detection with Wide FoV Using Roadside LiDAR
Abstract
:1. Introduction
- The detection range of base model is further extended. Roadside Lidar’s FoV could not be restrained by camera and can search targets in the whole 3D space;
- The omnidirectional detection results can be processed in parallel and generated by a 90° training model. There is no increased cost in the model training time and each result group is integrated into the same coordinate system;
- Overlapping object estimation and removal method are developed for point cloud switching, which can avoid false detections of the same object and offer accurate results.
2. Related Work
2.1. 2D Stitching Methods
2.2. 3D Stitching Methods
2.3. 3D Object Detection Methods
3. Methods
3.1. 3D Object Detection in Point Clouds
3.2. Generating Omnidirectional Detection Results
- Projecting the quadrangular prism to the plane coordinate system O-XY. The purpose of this is to simplify the origin model;
- Projecting the corners in far plane to Y-axis and the corresponding points are expressed as V1 and V2, as shown in Figure 3;
- According to location of 90° FoV, delimiting the rectangular FoV R-V1C2C3V2 to get the object attributes within the perspective range;
- Re-adjustment of the FoV is undertaken to optimize the detection range based on the voxel size. Extracting the Xmin, Ymin, Xmax and Ymax from R-V1C2C3V2 and setting the difference divided by the voxel size is a multiple of 16.
3.3. Overlapping Object Estimation and Removal
Algorithm 1 Duplicate target estimation and removal |
input: Detection result sets S1 (the front frames set) and S2 (the back frames set). |
output: A non-repeated data set S3 with 360°. |
1 for the cycle in length(S1) do: 2 for the single frame detection result s in S1 and S2 do: Build empty lists S1_frame and S2_frame to save each frame data. 3 if s[frame] = cycle do: set the uniform data format for s; add s to S1_frame or S2_frame. 4 end for |
5 Build an empty list del_list to store the indexes of duplicate targets. |
6 for each target s1 in S1 do: 7 for each target s2 in S2 do: Calculating CDIoU(s1,s2) 8 if CDIoU(s1,s2) ≤ 1 do: s1 and s2 overlap. Select the index of Min[score(s1), score(s2)] to save in the del_list. 9 else do: No overlap between s1 and s2. 10 end for 11 Remove multiple elements in the del_list. 12 end for |
13 Delete elements in the del_list from S1 and S2. |
14 S3 = S1 + S2 15 end for 16 return S3. |
4. Experiments
4.1. KITTI Dataset
4.2. Model Evaluation
4.3. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Location | Name | Example |
---|---|---|
1 | type | Car |
2 | truncated | 0 |
3 | occluded | 1 |
4 | alpha | 1.55 |
5–8 | bbox | (357.33, 133.24, 441.52, 216.2) |
9–11 | dimensions | (1.57, 1.32, 3.55) |
12–14 | location | (1.00, 1.75, 13.22) |
15 | rotation_y | 1.62 |
16 | score | 1.38 |
Easy (Imin = 0.70) | Moderate (Imin = 0.70) | Hard (Imin = 0.70) | Easy (Imin = 0.70) | Moderate (Imin = 0.50) | Hard (Imin = 0.50) | ||
---|---|---|---|---|---|---|---|
APR11 (Car) | bbox | 90.7746 | 89.6528 | 89.1511 | 90.7746 | 89.6528 | 89.1511 |
bev | 90.0448 | 87.2562 | 86.4370 | 90.7395 | 89.8508 | 89.5665 | |
3d | 88.9404 | 78.6675 | 77.8114 | 90.7395 | 89.8258 | 89.5203 | |
aos | 90.76 | 89.55 | 88.98 | 90.76 | 89.55 | 88.98 | |
APR40 (Car) | bbox | 96.4271 | 92.8655 | 90.5181 | 96.4271 | 92.8655 | 90.5181 |
bev | 93.1580 | 88.9119 | 86.7579 | 96.4536 | 95.1651 | 92.9492 | |
3d | 91.3287 | 80.5840 | 78.1417 | 96.4313 | 95.0479 | 92.8361 | |
aos | 96.41 | 92.74 | 90.34 | 96.41 | 92.74 | 90.34 |
Easy (Imin = 0.50) | Moderate (Imin = 0.50) | Hard (Imin = 0.50) | Easy (Imin = 0.50) | Moderate (Imin = 0.25) | Hard (Imin = 0.25) | ||
---|---|---|---|---|---|---|---|
APR11 (Pedestrian) | bbox | 73.5689 | 66.1255 | 62.2895 | 73.5689 | 66.1255 | 62.2895 |
bev | 67.1253 | 58.8610 | 53.3021 | 82.0378 | 74.9234 | 67.0553 | |
3d | 61.8900 | 54.4388 | 50.1242 | 81.9940 | 74.7333 | 66.9421 | |
aos | 70.86 | 63.01 | 59.00 | 70.86 | 63.01 | 59.00 | |
APR40 (Pedestrian) | bbox | 74.8557 | 67.7918 | 61.0786 | 74.8557 | 67.7918 | 61.0786 |
bev | 66.1146 | 58.1660 | 51.4365 | 82.8676 | 76.0780 | 68.8326 | |
3d | 62.8512 | 54.9202 | 47.9085 | 82.7933 | 74.8970 | 67.6176 | |
aos | 71.85 | 64.21 | 57.60 | 71.85 | 64.21 | 57.60 |
Easy (Imin = 0.50) | Moderate (Imin = 0.50) | Hard (Imin = 0.50) | Easy (Imin = 0.50) | Moderate (Imin = 0.25) | Hard (Imin = 0.25) | ||
---|---|---|---|---|---|---|---|
APR11 (Cyclist) | bbox | 89.6212 | 76.3985 | 70.1547 | 89.6212 | 76.3985 | 70.1547 |
bev | 85.7266 | 71.9324 | 66.3024 | 88.4910 | 74.5981 | 68.5349 | |
3d | 85.0175 | 66.8832 | 64.3491 | 88.4910 | 74.5981 | 68.5349 | |
aos | 89.54 | 75.90 | 69.74 | 89.54 | 75.90 | 69.74 | |
APR40 (Cyclist) | bbox | 94.9042 | 77.2506 | 72.8562 | 94.9042 | 77.2506 | 72.8562 |
bev | 90.3609 | 71.2446 | 68.1587 | 93.5190 | 75.2005 | 70.8290 | |
3d | 87.6985 | 68.6195 | 64.1195 | 93.5190 | 75.2005 | 70.8290 | |
aos | 94.82 | 76.71 | 72.33 | 94.82 | 76.71 | 72.33 |
PointRCNN (Base) | PV-RCNN | SECOND | Part-A2-Free | Ours | |
---|---|---|---|---|---|
Training time | 3 h | 5 h | 1.7 h | 3.8 h | 3 h |
Running time | 120.548 s | 102.653 s | 50.595 s | 93.343 s | 121.469 s |
Detection range | Front FoV | Front FoV | Front FoV | Front FoV | 360° FoV |
AP | 88.9404 | 89.3476 | 88.6137 | 89.1192 | 88.9404 |
Output objects | 4306 | 6094 | 8051 | 6259 | 7628 |
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Share and Cite
Lan, X.; Wang, C.; Lv, B.; Li, J.; Zhang, M.; Zhang, Z. 3D Point Cloud Stitching for Object Detection with Wide FoV Using Roadside LiDAR. Electronics 2023, 12, 703. https://doi.org/10.3390/electronics12030703
Lan X, Wang C, Lv B, Li J, Zhang M, Zhang Z. 3D Point Cloud Stitching for Object Detection with Wide FoV Using Roadside LiDAR. Electronics. 2023; 12(3):703. https://doi.org/10.3390/electronics12030703
Chicago/Turabian StyleLan, Xiaowei, Chuan Wang, Bin Lv, Jian Li, Mei Zhang, and Ziyi Zhang. 2023. "3D Point Cloud Stitching for Object Detection with Wide FoV Using Roadside LiDAR" Electronics 12, no. 3: 703. https://doi.org/10.3390/electronics12030703
APA StyleLan, X., Wang, C., Lv, B., Li, J., Zhang, M., & Zhang, Z. (2023). 3D Point Cloud Stitching for Object Detection with Wide FoV Using Roadside LiDAR. Electronics, 12(3), 703. https://doi.org/10.3390/electronics12030703