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Hi,
I notice that you generate instance masks for KITTI using the maskformer pretrained on coco-panoptic. I am wondering whether the domain gap between COCO and KITTI will lead to unsatisfied instance segmentation performances. Because I notice that the improvements on KITTI is not as drastic as on nuScenes.
The text was updated successfully, but these errors were encountered:
indeed, the coco model is not very good for KITTI. There are domain and class definition differences (e.g. we only have person in coco, but kitti 3d has both pedestrian / cyclists/ or person inside a vehicle). All of these makes training an KITTI segmentation model hard.
If you are interested in multimodal fusion on KITTI, one similar paper i noticed is https://github.com/LittlePey/SFD which also creates virtual points but filters the points based on 3d box proposal instead of image segmentation in our paper~(the latter is not very good due to reasons above)
Hi,
I notice that you generate instance masks for KITTI using the maskformer pretrained on coco-panoptic. I am wondering whether the domain gap between COCO and KITTI will lead to unsatisfied instance segmentation performances. Because I notice that the improvements on KITTI is not as drastic as on nuScenes.
The text was updated successfully, but these errors were encountered: