Cleaning of 3D Laser Scans with Machine Learning

April 21, 2021
|

3 min read

3D laser scan visualization of a building, highlighting areas for cleaning of 3D laser scans.
Cleaning of 3D Laser Scans Using Machine LearningIn this article, we introduce a semi-automated cleaning of 3D laser scans approach that incrementally trains a random forest (RF) classifier on an initial keep/discard point labeling generated by the user when cleaning the first scan(s). The classifier is then used to predict the labeling of the next scan in the sequence.

Terrestrial laser scanning campaigns provide an important means to document the 3D structure of historical sites. Unfortunately, the process of converting the 3D point clouds acquired by the laser scanner into a coherent and accurate 3D model has many stages and is not generally automated. In particular, the initial cleaning stage of the pipeline—in which undesired scene points are deleted—remains largely manual and is usually labour intensive.



Before this classification is presented to the user, a denoising post-process, based on the 2D range map representation of the laser scan, is applied. This significantly reduces small isolated point clusters that the user would otherwise have to fix. The user then selects the remaining incorrectly labelled points and these are weighted, based on a confidence estimate, and fed back into the classifier to retrain it for the next scan.

Our experiments, across 8 scanning campaigns, show that when the scan campaign is coherent, i.e., it does not contain widely disparate or contradictory data, the classifier yields a keep/discard labeling that typically ranges between 95% and 99%. This is somewhat surprising, given that the data in each class can represent many object types, such as a tree, person, wall, and so on, and that no further effort beyond the point labeling of keep/discard is required of the user.

We conducted an informal timing experiment over a 15-scan campaign, which compared the processing time required by our software, without user interaction (point label correction) time, against the time taken by an expert user to completely clean all scans. The expert user required 95mins to complete all cleaning. The average time required by the expert to clean a single scan was 6.3mins.

Even with current unoptimized code, our system was able to generate keep/discard labels for all scans, with 98% (average) accuracy, in 75 mins. This leaves as much as 20 mins for the user input required to relabel the 2% of mispredicted points across the set of scans before the full system time would match the expert’s cleaning time.

Click here for full reference of cleaning of 3D laser scans.

Get Lidar News in Your Inbox

Weekly updates on lidar tech, geospatial industry news, case studies, and product reviews.

About The Author

Gene Roe - founder of Lidar News

Phoenix Lidar System - complete lidar solutions
Phoenix Lidar Systems

Recent Point Cloud Processing Posts

Optimize Your Digital Workflow: Free Demo with Cintoo

Optimize Your Digital Workflow: Free Demo with Cintoo In this…

January 28, 2026

AI Hardware Revolutionizing Reality Capture Processes

AI is transforming reality capture almost as quickly as it…

December 11, 2025

Mining Stockpile Measurement Software: Stitch3D Wins $100K

Stitch3D has won $100K for its innovative mining stockpile measurement…

November 20, 2025

NUBIGON Point Cloud Visualization Transforms 3D Storytelling

Summary:NUBIGON’s point cloud visualization is redefining how reality capture data…

October 28, 2025

Rethinking Cloud Strategy for Spatial Data

Rethinking Cloud Strategy for Spatial Data For decades, the web…

September 25, 2025

LiDAR data processing and visualization made easy

LiDAR data processing and visualization made easy Emesent will present…

September 15, 2025

Popular Posts

Phoenix Lidar Systems

Get Lidar News in Your Inbox

Weekly updates on lidar tech, geospatial industry news, case studies, and product reviews.

Frontier Precision Unmanned