Real-time Progressive 3D Semantic Segmentation for Indoor Scene

Abstract

The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation. In this paper, we propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. To guarantee (near) real-time performance, our method is built atop an efficient super-voxel clustering method and a conditional random field with higher-order constraints from structural and object cues, enabling progressive dense semantic segmentation without any precomputation. We extensively evaluate our method on different indoor scenes including kitchens, offices, and bedrooms in the SceneNN and ScanNet datasets and show that our technique consistently produces state-of-the-art segmentation results in both qualitative and quantitative experiments.

arXiv commentWACV 2019. More information at https://pqhieu.github.io/wacv19.html

References in corpus (8)

• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Conditional Random Fields as Recurrent Neural Networks
• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
• ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
• Dynamic Graph CNN for Learning on Point Clouds
• PointCNN: Convolution On $\mathcal{X}$-Transformed Points
• Real-time Semantic Image Segmentation via Spatial Sparsity
• ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans

Cited by in corpus (3)

• Fusion++: Volumetric Object-Level SLAM
• JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds
• Semantic Dense Reconstruction with Consistent Scene Segments