Lidar Data augmentation
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I am working on Object Segmentation using Lidar 3D point clouds, basically using Kitti dataset. However, the dataset is imbalanced for classes cyclists and pedestrians. So, I am looking for ways to
1. Augment the point clouds to increase the dataset size for more instances of pedestrians and cyclists and
2. To create an unbiased model which is more accurate and robust
Can someone guide me as to how can I do it for point clouds, like augmentation techniques (apart from flipping in horizontal direction) or adding some kind of noise(awgn)/distortion to point clouds, while preserving the data consistency?
python deep-learning
asked 8 mins ago
pypropypro
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add a comment |
$begingroup$
I am working on Object Segmentation using Lidar 3D point clouds, basically using Kitti dataset. However, the dataset is imbalanced for classes cyclists and pedestrians. So, I am looking for ways to
1. Augment the point clouds to increase the dataset size for more instances of pedestrians and cyclists and
2. To create an unbiased model which is more accurate and robust
Can someone guide me as to how can I do it for point clouds, like augmentation techniques (apart from flipping in horizontal direction) or adding some kind of noise(awgn)/distortion to point clouds, while preserving the data consistency?
python deep-learning
asked 8 mins ago
pypropypro
1
New contributor
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am working on Object Segmentation using Lidar 3D point clouds, basically using Kitti dataset. However, the dataset is imbalanced for classes cyclists and pedestrians. So, I am looking for ways to
1. Augment the point clouds to increase the dataset size for more instances of pedestrians and cyclists and
2. To create an unbiased model which is more accurate and robust
Can someone guide me as to how can I do it for point clouds, like augmentation techniques (apart from flipping in horizontal direction) or adding some kind of noise(awgn)/distortion to point clouds, while preserving the data consistency?
python deep-learning
asked 8 mins ago
pypropypro
1
New contributor
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am working on Object Segmentation using Lidar 3D point clouds, basically using Kitti dataset. However, the dataset is imbalanced for classes cyclists and pedestrians. So, I am looking for ways to
1. Augment the point clouds to increase the dataset size for more instances of pedestrians and cyclists and
2. To create an unbiased model which is more accurate and robust
Can someone guide me as to how can I do it for point clouds, like augmentation techniques (apart from flipping in horizontal direction) or adding some kind of noise(awgn)/distortion to point clouds, while preserving the data consistency?
python deep-learning
python deep-learning
asked 8 mins ago
pypropypro
1
New contributor
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 mins ago
pypropypro
1
New contributor
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 mins ago
pypropypro
1
New contributor
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 8 mins ago
pypropypro
1
asked 8 mins ago
pypropypro
1
1
New contributor
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
pypro is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
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