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dynamic-object-removal (numpy-only)

Detector-free dynamic map cleaning via dynamic-object-removal — pip install from PyPI, numpy only.

Install

pip install "dynamic-object-removal>=0.5"

Run

From this folder (methods/dor_numpy/):

python main.py --data_dir /path/to/00 --algorithm fusion      # highest accuracy
python main.py --data_dir /path/to/00 --algorithm range
python main.py --data_dir /path/to/00 --algorithm scan_ratio
python main.py --data_dir /path/to/00 --algorithm temporal

fusion is the slowest of the four: ~11 min on seq 00 and ~29 min on seq 05 with the default --fusion-workers 6; the others run in a few minutes.

Evaluate

Each command writes dor_<algorithm>_output.pcd into data_dir. Export and score with the benchmark tools:

./build/export_eval_pcd /path/to/00 dor_fusion_output.pcd 0.05
python scripts/py/eval/evaluate_all.py

evaluate_all.py reads its Result_Folder, algorithms, and all_seqs settings from the constants at the top of the file — add dor_fusion (or the algorithm you ran) to the algorithms list before running it.

Semantic-KITTI teaser results (seq 00 / 05)

algorithm seq 00 SA seq 00 DA seq 00 AA seq 05 SA seq 05 DA seq 05 AA
fusion 98.9 98.3 98.6 98.0 98.1 98.0
range 99.6 34.5 58.6 99.8 25.9 50.9
scan_ratio 98.0 92.8 95.4 96.0 97.9 96.9
temporal 97.0 46.6 67.2 97.3 25.9 50.2

fusion (library v0.5.0) OR-combines three evidence channels computed per scan against the accumulated map: ray-sampled free-space carving with per-scan hit precedence, DUFOMap-style eroded void confirmation (hit inflation + full 26-neighborhood erosion), and the scan_ratio votes at a stricter fraction. The channels fail in complementary regimes — fractional free-space voting nails transient traffic (seq 00), absolute void counts catch slow movers and late leavers (seq 05) — so the union scores high on both.

scan_ratio normalizes votes per point: a map point is removed only when a majority of the scans that actually revisit its polar column flag it as vacated (library default since v0.4.0). Rarely-observed static points no longer accumulate spurious votes over the sequence, which lifts SA to ~96-98% at near-unchanged DA.

Reproduce end-to-end (download + eval) from the upstream library repo:

git clone https://github.com/rsasaki0109/dynamic-3d-object-removal.git
python3 dynamic-3d-object-removal/scripts/run_dynamicmap_benchmark.py --sequences 00 05