michaelramamonjisoa / sharpnet Goto Github PK

In demo.py :

depth_pred = depth_pred.data.cpu().numpy()[0, 0, ...]
depth_pred = scale * cv2.resize(depth_pred* 65535 / 1000, dsize=(image_original.size[0], image_original.size[1]), interpolation=cv2.INTER_LINEAR)

Change to :

depth_pred = depth_pred.data.cpu().numpy()[0, 0, ...]
depth_pred = scale * cv2.resize(depth_pred*255, dsize=(image_original.size[0], image_original.size[1]), interpolation=cv2.INTER_LINEAR)

Regarding 6.4 in the paper, do you use actual weight decay or a simple L^2 regularisation term on the weights? Is the optimizer ordinary SGD or something like Adam?

If the size of my input image is not 640x480, can you tell me how to set the --scale option ？an example？

Hello,
I load the model pre-trained on PBRS, then finetune on NYU. But when I run demo.py, the model can't predict occluding contours, the predicted result is blank. However, the other two task can work normally. I'm confused......

I mean the code of implementation of Figure 1 of https://arxiv.org/pdf/1905.08598.pdf.
Thank you for reply!

Thank you for sharing your great work.
I have one question about the implementation of your loss function. For depth-boundary consistency loss, I think you realize it as a class there. But I found it isn't the same as the formulation you use in your paper. It seems like one the first term and without a minus sign. I'm a little confused and please help me figure it out.

In the dataset_manager.py file you normalize the depth map differently for the PBRS dataset and the NYU-V2 datasets.

More precisely, in the PBRS dataset you divide the depth maps by 65535 whereas in the NYUV2 dataset you use 65.535.

Is there a specific reason behind these normalizations ?

Thank you very much

Hi, I have been watching your excellent work recently. However I have not found the loss functions computation in your source code. Could you share them for us to take a look?

I noted that you manually annotated 100 NYUv2 images with occlusion contours. Can you kindly tell me which tool did you use for annotation? Thanks a lot!

As shown in the following lines, the only available dataset, NYU, did not load normal (and boundary) GT? I also check and find normal loss (and geo loss) are 0. However, in Fig. 7, normal GTs are showed. Is there way to load normal GT from NYU dataset? Thanks!

https://github.com/MichaelRamamonjisoa/SharpNet/blob/master/utils.py#L141-L142

Hi, Michael,

Nice idea and great work! I am wondering if you have result of KITTI. Or do you plan to further work on KITTI?

Thanks

Thank for this wonderful work!
I believe there is a bug in this line of code.

Reference: BSDS500, subset test. Order: 1st row - RGB, Depth, 2nd row - Boundary, Normals

I tried out the BSDS500 dataset (https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html) and got these artefacts always in the left bottom in about 20% of 200 images from the test subset.

I found the following code in file 'demo.py'
depth_pred = depth_pred.data.cpu().numpy()[0, 0, ...] * 65535 / 1000

and I think the depth_pred is the real depth in meters, right?