Cannot get the right depth on kitti

[yvanyin]

Cannot get the right depth on kitti

[YvanYin]

python ./tools/test_kitti_metric.py
--dataroot ./datasets/KITTI
--dataset kitti
--cfg_file lib/configs/resnext101_32x4d_kitti_class.yaml
--load_ckpt ./kitti.pth

You can try the above script. But you should change the --dataroot and --dataset. In ./tools/test_any_images.py, I have scaled the output depth with 60000 for visualization. If you scale the output depth with 80 again, the max depth will be greater than 65535.

Besides, you can save the depth image in a coded color map for visualization:

import matplotlib.pyplot as plt
plt.imsave('path to save', depth, cmap='rainbow')

It is easier for you to check if the depth output is right.

[kunlileo]

python ./tools/test_kitti_metric.py
--dataroot ./datasets/KITTI
--dataset kitti
--cfg_file lib/configs/resnext101_32x4d_kitti_class.yaml
--load_ckpt ./kitti.pth

You can try the above script. But you should change the --dataroot and --dataset. In ./tools/test_any_images.py, I have scaled the output depth with 60000 for visualization. If you scale the output depth with 80 again, the max depth will be greater than 65535.

Besides, you can save the depth image in a coded color map for visualization:

import matplotlib.pyplot as plt
plt.imsave('path to save', depth, cmap='rainbow')

It is easier for you to check if the depth output is right.

Hi Wei Yin,

i am quite surprised to have received your reply in such a short Time. Thank you very much.

I have already adjusted the codes in "test_any_images.py" in the way, where i left out the scaling with 60000. Instead, I just took the pred_depth from the pretrained model's output and multiplied it with 80. The result is what i attached above. But I will try your suggestion with "test_kitti_metric.py" tomorrow and keep you informed.

Greetings from Germany.

Kun

[kunlileo]

python ./tools/test_kitti_metric.py
--dataroot ./datasets/KITTI
--dataset kitti
--cfg_file lib/configs/resnext101_32x4d_kitti_class.yaml
--load_ckpt ./kitti.pth

You can try the above script. But you should change the --dataroot and --dataset. In ./tools/test_any_images.py, I have scaled the output depth with 60000 for visualization. If you scale the output depth with 80 again, the max depth will be greater than 65535.

Besides, you can save the depth image in a coded color map for visualization:

import matplotlib.pyplot as plt
plt.imsave('path to save', depth, cmap='rainbow')

It is easier for you to check if the depth output is right.

Hi Wei Yin,

it turns out I made a very stupid mistake. I misstook the output of your model for Disparity, instead of Depth. After adjusting my visualization script, I get the right result, which shows more promising pointcloud than PSMNet. Terrific Work and merry chrismas!

(Pointcloud generated by disparity map from PSMNet)

(Pointcloud generated by depth map from VNL)

[YvanYin]

Merry Chrismas!