Dynamic 3D Object Removal

No GPU, numpy-only, geometry-based. This library removes dynamic objects from LiDAR point clouds without deep learning. For single scans it uses 3D bounding box cropping. For multi-frame sequences it uses voxel-based temporal filtering to reduce moving-object contamination.

Start Here

• AV2 public sequence demo: https://rsasaki0109.github.io/dynamic-3d-object-removal/demo/index_3d_sequence_av2.html
• Single-scan demo: https://rsasaki0109.github.io/dynamic-3d-object-removal/demo/index_3d_standalone.html
• Local sequence proof demo: https://rsasaki0109.github.io/dynamic-3d-object-removal/demo/index_3d_sequence_standalone.html

What this repo is trying to prove first:

• You can compare a pose-aligned 20-frame AV2 accumulated map
• You can remove 233k ghost points (11.9%) from a 2M-point raw accumulation
• Dynamic trails are reduced while roads, buildings, and other static structure remain

These two hero images are not single scans. They show a 20-frame accumulated map. For single-scan removal, see Quick Start and the Single-scan demo.

20-frame accumulated map from Argoverse 2 real data. This is not a single-scan comparison. It is a map-level ghost cleanup proof showing 233k ghost points removed (11.9%).

Features

• No deep learning: give it detected 3D boxes and it removes points geometrically. No GPU, training data, or model inference required
• Fast: 1.5 ms for 24k points on CPU
• ROS2 realtime node: subscribe to PointCloud2, filter, and publish. Supports both box and temporal algorithms
• Minimal dependencies: numpy only. pyarrow is only needed for Argoverse 2 Feather input
• Public proof artifacts: checked-in single-scan, local sequence proof, and AV2 public sequence demos

What the sequence demos are meant to show:

• Raw accumulation creates ghost contamination
• Cleaned accumulation reduces it
• Stable static structure is preserved

Notes:

• The checked-in local sequence proof demo does not ship per-frame box JSON, so its cleaned side is generated with temporal consistency
• The AV2 public sequence demo uses annotations.feather and city_SE3_egovehicle.feather for pose-aligned, box-driven accumulation
• If per-frame boxes exist, pass --input-objects to regenerate a box-driven sequence
• If you want multiple frames aligned into a shared map frame, also pass --input-poses

Installation

git clone https://github.com/rsasaki0109/dynamic-3d-object-removal.git
cd dynamic-3d-object-removal
python3 -m pip install -e .

Quick Start On Public Data

You can try dynamic object removal on real Argoverse 2 data in three commands. No signup is required.

# 1. Download an Argoverse 2 sample (1 sweep + annotations, ~1.3 MB)
pip install awscli pyarrow
python3 scripts/download_av2_sample.py

# 2. Remove dynamic objects (18 vehicles, 3 pedestrians, 1 bicycle, 1 wheelchair)
dynamic-object-removal \
  --input-cloud data/av2_sample/lidar/315969904359876000.feather \
  --input-objects data/av2_sample/annotations.feather \
  --timestamp-ns 315969904359876000 \
  --output-cloud output/av2_cleaned.pcd

# 3. Inspect before/after in 3D
python3 demo/run_scan_demo.py \
  --input-cloud data/av2_sample/lidar/315969904359876000.feather \
  --input-objects data/av2_sample/annotations.feather \
  --timestamp-ns 315969904359876000 \
  --max-render-points 50000 \
  --output-html demo/index_3d_av2.html

Removes 3,406 points out of 95,381 (3.6%). Vehicles, pedestrians, and bicycles disappear while static road and building structure remains.

KITTI is also supported. See scripts/download_kitti_sample.py.

Demo Regeneration

Single Scan

python3 demo/run_scan_demo.py \
  --input-cloud demo/actual_scan_20240820_cloud.pcd \
  --input-objects demo/actual_scan_20240820_objects.json \
  --max-render-points 220000 \
  --output-scene demo/demo_scene_single_scan.json \
  --output-html demo/index_3d_standalone.html

Sequence

python3 demo/run_scan_sequence_demo.py \
  --input-glob "/path/to/graph/*/cloud.pcd" \
  --frame-count 12 \
  --stride 1 \
  --max-render-points 9000 \
  --fps 4 \
  --voxel-size 0.35 \
  --window-size 5 \
  --min-hits 3 \
  --output-html demo/index_3d_sequence_standalone.html

• Pass --input-objects to build the cleaned side from per-frame box removal
• --input-objects accepts either a single box payload or a frame name -> payload JSON map
• Use --input-objects /path/to/annotations.feather --input-poses /path/to/city_SE3_egovehicle.feather to generate the AV2 public sequence in a shared map frame
• The checked-in HTML files are self-contained and embed sampled point data directly

CLI

dynamic-object-removal \
  --input-cloud /path/to/scan.pcd \
  --input-objects /path/to/objects.json \
  --output-cloud /path/to/output.xyz

dynamic-object-removal --help

ROS2 Realtime Node

The realtime node subscribes to PointCloud2, filters it, and publishes cleaned points.

# Box-driven removal with an external detector
dynamic-object-removal-realtime \
  --pointcloud-topic /velodyne_points \
  --objects-topic /detected_objects \
  --output-topic /cleaned_points \
  --algorithm box

# Temporal consistency without a detector
dynamic-object-removal-realtime \
  --pointcloud-topic /velodyne_points \
  --output-topic /cleaned_points \
  --algorithm temporal \
  --voxel-size 0.10 --temporal-window 5 --temporal-min-hits 3

dynamic-object-removal-realtime --help

Library API

from pathlib import Path
from dynamic_object_removal import load_points, load_boxes, remove_points_in_boxes, save_points

points = load_points(Path("/path/to/scan.pcd"), fmt="auto")
boxes = load_boxes(Path("/path/to/objects.json"), fmt="auto", skip_invalid=True)
kept, keep_mask = remove_points_in_boxes(points, boxes, margin=(0.05, 0.05, 0.05))

save_points(Path("/path/to/output.xyz"), kept, fmt="auto")

Main public APIs:

• load_points(path, fmt="auto")
• load_boxes(path, fmt="auto", skip_invalid=False)
• remove_points_in_boxes(points, boxes, margin=(0.05, 0.05, 0.05))
• TemporalConsistencyFilter(voxel_size=0.10, window_size=5, min_hits=3)
• save_points(path, fmt="auto")

Supported Formats

• Point clouds: PCD (ASCII / binary), CSV, TXT, XYZ, NPY, BIN (KITTI), Feather (Argoverse 2)
• Bounding boxes: JSON, CSV, KITTI label_2, Feather (Argoverse 2)
• PCD DATA binary_compressed is not supported

Related Work

• UTS-RI/dynamic_object_detection