Comments (4)
Excuse me, I would like to ask you how to test your own code?
from dcp.
Hi, sorry to bother. When I test it with a model cloud as a souce and a transformed cloud(by random rotation and translation) as target, it works amazingly.
But when I use a incomplete point cloud(a RGBD projection to 3D, incomplete here is point cloud from one perspective) as source and a complete model in the RGBD, it works poorly. I wonder it is my mistaken operation or the model itself? Will it perform as perfectly as it is on complete ones now if I train it with incomplete point cloud?
Thanks!
Best
Cheng
Hi Cheng,
Yes. There is a gap between the training and testing. If you use the pretrained model, which is trained on ModelNet40, it's highly likely to fail on scans. I would suggest training on the data you collect, and then testing. We're also working on partial point cloud matching, stay tuned. :-)
Best,
Yue
from dcp.
Many thanks! I'll try it!
from dcp.
Many thanks! I'll try it!
can you tell me how the networl perform on incomplete point cloud?
from dcp.
Related Issues (20)
- Is there any version using tensorflow? HOT 1
- why pretrainedModel "dcp_v2.t7" include more parameters? HOT 1
- how to run my own dataset HOT 7
- Questions related to the performance of DCP and ICP HOT 2
- typo in the title of readme: Deep **Cloeset** Point. HOT 1
- Can I train a model as good as the pre-trained model? HOT 10
- GTX1060 CUDA out of memory HOT 1
- Pretrained model for unseen data HOT 4
- How to prepare my own data set for the DCP training? HOT 3
- why get_graph_feature applies only once in this project compared with dgcnn? HOT 1
- some question about the method to process the pointcloud HOT 1
- DCP for two different point clouds HOT 4
- I want to know what the distance metric of features you use HOT 1
- Visualization HOT 2
- Test dataset is used for model selection
- The performance decreases dramatically if the point cloud sampling is changed HOT 3
- How about training point clouds with different size? HOT 1
- Yes, just follow the instructions given in the README with the pretrained model. To run inference on the whole dataset -
- RuntimeError: CUDA error: device-side assert triggered
- Visualization problem of network intermediate output HOT 1
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