Robbyant / Robbyant/lingbot-map
Question about reproducing the 7-Scenes benchmark settings
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Description
Hi, thank you for releasing this cool work and the codebase.
I understand that an official evaluation benchmark may be released later,
but in the meantime I am trying to reproduce the 7-Scenes numbers reported in the paper.
In particular, I am looking at the 7-Scenes results in Table 4 and Table 5:
- Pose:
AUC@3 = 12.63,AUC@30 = 78.59,ATE = 0.08 - Reconstruction:
Acc = 0.02,Comp = 0.07,F1 = 80.39
With my current setup, I get lower numbers than the paper, so I suspect that my evaluation/inference protocol may not match yours.
The command I used is roughly:
python demo.py \
--model_path ./models/lingbot-map.pt \
--image_folder /path/to/7scenes/<scene>/<seq> \
--image_ext ".color.png" \
--stride 5 \
--mode streaming \
--image_size 518 \
--patch_size 14 \
--num_scale_frames 8 \
--no_visualization \
--save_predictions /path/to/output.pt
For reconstruction evaluation, I used voxel size 4.0 / 512, ICP threshold 0.1, and F1 threshold 0.05, following the paper description as closely as possible.
Could you share the approximate settings used to obtain the reported 7-Scenes results? In particular, I would like to confirm:
- Which checkpoint was used for Table 4/5: lingbot-map.pt, lingbot-map-long.pt, or another internal checkpoint?
- Was 7-Scenes evaluated in pure streaming mode, windowed mode, or with any state reset policy?
- What keyframe interval / cache policy / number of scale frames was used?
- Were the reconstruction metrics computed from the predicted world_points, or by unprojecting predicted depth with predicted poses?
- Are there any confidence filtering, sky/depth masking, pose convention, or alignment details that are important for matching the reported numbers?
Thanks again for the great work.
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Research direction
Start with demo.py and the reported Table 4 and Table 5 values, then compare the supplied command and reconstruction settings with the repository's documented evaluation path. Check the checkpoint, inference mode, state-reset and keyframe settings, metric inputs, and filtering or alignment details mentioned in the issue. Done means the settings needed to reproduce the reported 7-Scenes results are identified and documented.
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Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 32/100