Megvii-BaseDetection / Megvii-BaseDetection/BEVDepth
some concepts that not appear at paper
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 881
- Forks
- 124
- PR merge metrics
- No merged PRs in 30d
Description
hello! thanks for your great works!
when i go into source code, i found "sweep", "ida" and "bda". these maybe something we have not seen in LSS. can you kindly explain the meaning of the three concepts? thanks!
--- update ---
sorry for my not familiar with nuscenes data. i google and found sweep images are not annotated intermediate frames (the counterpart of annotated keyframe). maybe the first sweep image (index 0 of sweep axis) in your code is keyframe. is that correct?
but i still not know about "ida" and "bda". hope you can kindly make some explanation for them. thanks!
for example in LSSFPN
def forward(self,
sweep_imgs,
mats_dict,
timestamps=None,
is_return_depth=False):
"""Forward function.
Args:
sweep_imgs(Tensor): Input images with shape of (B, num_sweeps,
num_cameras, 3, H, W).
mats_dict(dict):
# ...
ida_mats(Tensor): Transformation matrix for ida with
shape of (B, num_sweeps, num_cameras, 4, 4).
# ...
bda_mat(Tensor): Rotation matrix for bda with shape
of (B, 4, 4).
# ...
"""
batch_size, num_sweeps, num_cams, num_channels, img_height, \
img_width = sweep_imgs.shape
# ... ...
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with LSSFPN.forward and the mats_dict docstring shown in the issue, then trace where sweep_imgs, ida_mats, and bda_mat are defined and used. Done means adding project documentation that explains sweep, ida, and bda, including the relationship between sweeps and keyframes, without requiring readers to infer their meanings from source code.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, documentation
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100