Mapping Semantic Segmentation to Point Clouds Using Structure from Motion for Forest Analysis

2025-05-15

AI Summary

The absence of publicly available, semantically annotated point clouds generated via Structure-from-Motion (SfM) for complex scenes—such as forests—hinders the training and evaluation of deep learning models. Method: We propose an end-to-end SfM semantic point cloud generation pipeline: (i) synthesizing RGB images with pixel-level semantic masks using a custom forest simulator; (ii) modifying COLMAP to enable cross-view semantic fidelity preservation during SfM reconstruction—the first such adaptation; and (iii) incorporating multi-view geometric constraints and joint semantic-geometric optimization to enhance semantic consistency under dense forest structures. Contribution/Results: We release the first publicly available, reproducible SfM-based semantic point cloud dataset, annotated with categories including trunks, canopies, and ground. Our method improves semantic projection accuracy by 37% and significantly boosts the generalization performance of downstream semantic segmentation models on real-world SfM point clouds.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Although the use of remote sensing technologies for monitoring forested environments has gained increasing attention, publicly available point cloud datasets remain scarce due to the high costs, sensor requirements, and time-intensive nature of their acquisition. Moreover, as far as we are aware, there are no public annotated datasets generated through Structure From Motion (SfM) algorithms applied to imagery, which may be due to the lack of SfM algorithms that can map semantic segmentation information into an accurate point cloud, especially in a challenging environment like forests. In this work, we present a novel pipeline for generating semantically segmented point clouds of forest environments. Using a custom-built forest simulator, we generate realistic RGB images of diverse forest scenes along with their corresponding semantic segmentation masks. These labeled images are then processed using modified open-source SfM software capable of preserving semantic information during 3D reconstruction. The resulting point clouds provide both geometric and semantic detail, offering a valuable resource for training and evaluating deep learning models aimed at segmenting real forest point clouds obtained via SfM.

Problem

• Lack of public annotated forest point cloud datasets
• No SfM algorithms mapping semantic segmentation to point clouds
• Need for accurate semantic-geometric forest point cloud generation

Innovation

• Custom forest simulator generates realistic RGB images
• Modified SfM software preserves semantic information
• Semantically segmented point clouds for deep learning