Closed raabuchanan closed 1 year ago
Hi, thanks for your interest in our work!
Please let me know if you need further help!
Thank you for the response, I'll just clarify what I mean for question 2.
Basically I wanted to visualize the depth and RGB images used for computing loss. When I look at gt_depth
and gt_rgb
by adding the following code:
plt.subplot(2, 2, 1)
plt.title('gt rgb image')
plt.imshow(gt_rgb.cpu())
plt.subplot(2, 2, 2)
plt.title('gt depth image')
plt.imshow(gt_depth.cpu())
plt.subplot(2, 2, 3)
plt.title('rgb image')
plt.imshow(rgb.cpu())
plt.subplot(2, 2, 4)
plt.title('depth image')
plt.imshow(depth.cpu())
plt.show()
I get the attached output which shows garbled images for gt_depth
and gt_rgb
. I would have expected images generated by the MLP to look closer to the input images.
The training samples are obtained from function get_training_samples. The gt pixels are sampled from a subset of pixels (number = cfg.n_samples_per_frame
) from training frames. And the training frames (number = cfg.win_size
) are sampled from a keyframe buffer. Therefore, the visualisation of training samples wouldn't look like an image, and will actually be a group of pixels from the historical observations instead.
If you want to visualise a rendering image with the gt rgb, you need to render a whole image by a given pose.
And reduce the mesh grid resolution here will also speed up the meshing speed.
Ah ok I think I understand now, thanks
Hi, thanks for open sourcing your code!
I'm just trying to get iMAP running and I have two questions:
Thank you