A realistic perception through Lidar

  • b05902031謝議霆
  • b05902008王行健
  • b05902125葛淯為

Outline

  1. Lidar Data format
  2. object detection
  3. PIXOR
  4. Deep continous fusion
  5. Frustum pointnets
  6. Aggregate View object detection
  7. conclusion

Lidar data format

Outline

  1. Lidar Data format
  2. object detection
  3. PIXOR
  4. Deep continous fusion
  5. Frustum pointnets
  6. Aggregate View object detection
  7. conclusion

3D point cloud

https://deepdrive.berkeley.edu/sites/default/files/styles/project_primary/public/projects/1707ObjectDetection_DataCollected.png?itok=x_B2igjd&c=6bdf32aba2a1789b077401c605966b17

BEV(bird eye view)

https://www.google.com/url?sa=i&source=images&cd=&cad=rja&uact=8&ved=2ahUKEwi06cnP8q7fAhWPd94KHetfBCQQjRx6BAgBEAU&url=http%3A%2F%2Fronny.rest%2Fblog%2Fpost_2017_03_26_lidar_birds_eye%2F&psig=AOvVaw2QVG5xJaiLwknrmXxMzn3a&ust=1545411818858203

Object detection

Outline

  1. Lidar Data format
  2. object detection
  3. PIXOR
  4. Deep continous fusion
  5. Frustum pointnets
  6. Aggregate View object detection
  7. conclusion

BEV

https://www.google.com/url?sa=i&source=images&cd=&ved=2ahUKEwi6vaHi8q7fAhVQZt4KHTqQCkcQjRx6BAgBEAQ&url=https%3A%2F%2Farxiv.org%2Fpdf%2F1805.01195&psig=AOvVaw3NDCkHDkL3oMDAH5rwL4ea&ust=1545411981064996

Image

http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf

Evaluation -- IOU

  • Intersection of union
Predicted\ region\cap ground\ truth \over Predicted\ region \cup ground\ truth
Predicted region∩ground truthPredicted region∪ground truthPredicted\ region\cap ground\ truth \over Predicted\ region \cup ground\ truth
  • Information in IOU
    • Center
    • angle
    • size

PIXOR

real-time object detection on BEV

http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/3012.pdf

Outline

  1. Lidar Data format
  2. object detection
  3. PIXOR
  4. Deep continous fusion
  5. Frustum pointnets
  6. Aggregate View object detection
  7. conclusion

Comparison

  • Sparse point
  • 3D convolution
  • costly

3D clouds

BEV

  • Depth channel
  • standard convolution
  • fast

Result

Vehicle Pedestrian Bicyclist
AP_{0.5}
AP0.5AP_{0.5}
AP_{0.7}
AP0.7AP_{0.7}
AP_{0.3}
AP0.3AP_{0.3}
AP_{0.5}
AP0.5AP_{0.5}
AP_{0.3}
AP0.3AP_{0.3}
AP_{0.5}
AP0.5AP_{0.5}
91.35
91.3591.35
79.37
79.3779.37
n/a
n/an/a
n/a
n/an/a
n/a
n/an/a
n/a
n/an/a

What really matters?

Is it important to detect a car?

Improper trade off

  • Connected Car
  • pedestrian, bicyclist
  • restrict in BEV
  • misunderstand object

Deep Continuous Fusion

http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf

Outline

  1. Lidar Data format
  2. object detection
  3. PIXOR
  4. Deep continous fusion
  5. Frustum pointnets
  6. Aggregate View object detection
  7. conclusion

Multi-Sensor

  • To improve the disadvantage of lidar data
  • Non-trivial fusion between 3D point cloud and camera image
  • Sparse VS continous

method

Result

Vehicle Pedestrian Bicyclist
PIXOR
Cont Fusion
AP_{0.5}
AP0.5AP_{0.5}
AP_{0.7}
AP0.7AP_{0.7}
AP_{0.3}
AP0.3AP_{0.3}
AP_{0.5}
AP0.5AP_{0.5}
AP_{0.3}
AP0.3AP_{0.3}
AP_{0.5}
AP0.5AP_{0.5}
91.35
91.3591.35
79.37
79.3779.37
n/a
n/an/a
n/a
n/an/a
n/a
n/an/a
n/a
n/an/a
94.94
94.9494.94
83.89
83.8983.89
82.32
82.3282.32
75.34
75.3475.34
74.08
74.0874.08
59.83
59.8359.83

Issue

  • non-observable place in Camera Image
  • Solution : KNN search

Frustum PointNets

http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf

Outline

  1. Lidar Data format
  2. object detection
  3. PIXOR
  4. Deep continous fusion
  5. Frustum pointnets
  6. Aggregate View object detection
  7. conclusion

Another fusion method

  • RGB image + Depth
  • Depth channel : From 3D point cloud

Segmentation

  • candidate
  • Time reduction : f-rcnn to reduce the candidates

Classify

  • Class
  • orientation
  • center
  • size

Two stage

Label the box

  • In RGBD image, a box might include some point with non-reasonable depth
  • Thus, label on the 3D image instead.

Aggregate View Object Detection

https://arxiv.org/pdf/1712.02294.pdf

Outline

  1. Lidar Data format
  2. object detection
  3. PIXOR
  4. Deep continous fusion
  5. Frustum pointnets
  6. Aggregate View object detection
  7. conclusion

Pyramid-like structure

  • sparse point
    • points of pedestrian, small object......
  • different level of kernel

A set of anchor grid

  • Possible box
  • Score a region to choose the candidate
  • Time reduction

Conclusion

Top among methods

  • speed : PIXOR
  • Vehicle : continuous fusion
  • Pedestrian & bicyclist : Frustum pointnets

Difficulty to be realistic

  • Attractive factor : Score (time, accuracy)
  • Cost to achieve such score
  • zero-tolerant on error

Lidar

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