Comments (1)
qianqianwang68
commented on August 16, 2024
1
Thank you for your question. You may consider reducing the number of samples on a ray (--num_samples_ray), setting --use_affine to False, or downsampling your videos to smaller resolutions.
Alternatively, depending on the specific tasks you are thinking about, other works might be useful too. You may consider feedforward approaches for dense and long-range tracking that don't require per-video optimization and thus are much faster, like TAPIR (https://deepmind-tapir.github.io/) and CoTracker (https://co-tracker.github.io/)
from omnimotion.
Related Issues (20)
- preparation of custom data set
- Training and evaluating model on TAP-Vid DAVIS produces different results HOT 10
- Reporting mistakes during training HOT 2
- A question about blending weight. HOT 9
- The TAPNet loader Module
- The given checkpoint do not match all the model, and it's hard to reproduce the result HOT 6
- Question about the depth consistency loss HOT 2
- Hello! This is a question about how to perform online operations after training is complete. HOT 3
- Train all frames or sample some? HOT 2
- Particle Tracking Results HOT 3
- Does it have to be trained and optimized for every new video? HOT 1
- the frame resolution when evaluating on TAP-Vid HOT 1
- Will the model weights and testing code be open-sourced HOT 1
- Transformation matrix
- Evaluate the trained checkpoints and the provided checkpoints, and the results of the metrics are inconsistent
- What may be the reason for not generating visual trajectories.
- Can I transform the input of INN into three-dimensional coordinates with an additional fixed fourth dimension?
- track fast moving objects
- Could you share the code for RAFT-C and RAFT-D evaluation in table 1? Thank you! HOT 1
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