3D Instance Segmentation of MVS Buildings

TL;DR

This paper introduces a new 3D instance segmentation framework for urban buildings using multi-view images, effectively handling attached and embedded structures, and provides a novel annotated dataset for outdoor 3D segmentation.

Contribution

The work presents a novel multi-view 3D instance segmentation method for urban buildings and introduces the first outdoor dataset with detailed 3D building annotations.

Findings

Effective segmentation of attached 3D buildings in urban scenes.

Multi-view framework outperforms orthophoto-based methods.

New dataset enhances research in 3D urban scene analysis.

Abstract

We present a novel 3D instance segmentation framework for Multi-View Stereo (MVS) buildings in urban scenes. Unlike existing works focusing on semantic segmentation of urban scenes, the emphasis of this work lies in detecting and segmenting 3D building instances even if they are attached and embedded in a large and imprecise 3D surface model. Multi-view RGB images are first enhanced to RGBH images by adding a heightmap and are segmented to obtain all roof instances using a fine-tuned 2D instance segmentation neural network. Instance masks from different multi-view images are then clustered into global masks. Our mask clustering accounts for spatial occlusion and overlapping, which can eliminate segmentation ambiguities among multi-view images. Based on these global masks, 3D roof instances are segmented out by mask back-projections and extended to the entire building instances through a Markov random field optimization. A new dataset that contains instance-level annotation for both 3D urban scenes (roofs and buildings) and drone images (roofs) is provided. To the best of our knowledge, it is the first outdoor dataset dedicated to 3D instance segmentation with much more annotations of attached 3D buildings than existing datasets. Quantitative evaluations and ablation studies have shown the effectiveness of all major steps and the advantages of our multi-view framework over the orthophoto-based method.