youquanl / segment-any-point-cloud Goto Github PK

Dear authors,
Thank you for your excellent work! I cannot find the number of time frames you used in the paper. Can you provide the number?

Hi, thanks for this fantastic project.

C2L refers to Camera-to-LiDAR distillation in Table.5 of the paper.

Is there any ablation study on the impact of the quality of C2L on performance?
As we know, the superpoints are generated by C2L from superpixels.
If there are larger time offset between lidar and camera data, there would be very few superpoints left due to mismatching.
eg (the pink color are projected lidar points):

Since it is common that 2d-to-3d correspondence can be really bad in reality, it would be great if this project can overcome this issue.

Thanks for your amazing work and shared code. I find it is slow to generate the superpixels on my server. Could you share the files of generated superpixels with SEEM for me? @ldkong1205 @youquanl

Thanks for sharing. Recently, using your code, I generated the superpixels with SEEM. Then I pre-trained the 3D backbone with SLidR with the new superpixels. However, the performance is not as good as that in paper. Specifically, I finetuned the model on 1% nuScenes dataset, the mIoU is 41.0, but the claimed result in the paper is 44.02 m_IoU. Can you give me some suggestions? Thanks very much.

Thanks for sharing this cool project! I was confused by how you segment 2D image using VFMs:

As a result, SAM is able to segment images, with either point, box, or mask prompts, across different domains and data distributions. (from 6.2 Vision Foundation Models)

What did you feed to SAM to get the final segmented 2D image?

Thanks for your explanation.

Thank you for your excellent work. I would like to ask when the 3D backbone MinkUNet after pre-training can be open source? Thank you very much！