OccuSeg: Occupancy-aware 3D Instance Segmentation (2003.06537v3)

Abstract: 3D instance segmentation, with a variety of applications in robotics and augmented reality, is in large demands these days. Unlike 2D images that are projective observations of the environment, 3D models provide metric reconstruction of the scenes without occlusion or scale ambiguity. In this paper, we define "3D occupancy size", as the number of voxels occupied by each instance. It owns advantages of robustness in prediction, on which basis, OccuSeg, an occupancy-aware 3D instance segmentation scheme is proposed. Our multi-task learning produces both occupancy signal and embedding representations, where the training of spatial and feature embeddings varies with their difference in scale-aware. Our clustering scheme benefits from the reliable comparison between the predicted occupancy size and the clustered occupancy size, which encourages hard samples being correctly clustered and avoids over segmentation. The proposed approach achieves state-of-the-art performance on 3 real-world datasets, i.e. ScanNetV2, S3DIS and SceneNN, while maintaining high efficiency.

Overview of "OccuSeg: Occupancy-aware 3D Instance Segmentation"

The paper "OccuSeg: Occupancy-aware 3D Instance Segmentation" by Lei Han, Tian Zheng, Lan Xu, and Lu Fang, introduces a novel approach for instance segmentation in 3D space. This research is situated within the growing field of 3D geometric modeling and deep learning, showing significant promise for applications in robotics and augmented reality. The authors highlight the inadequacies of mapping 2D image segmentation techniques directly to 3D data, which often result in poor performance due to the absence of spatial depth and occupancy awareness.

Methodological Innovations

OccuSeg operates by introducing a "3D occupancy signal," a pioneering concept that represents the number of voxels occupied by each instance within a 3D environment. This signal serves as a robust foundation for segmenting instances more accurately in three-dimensional space, addressing typical challenges such as occlusion and scale ambiguity. The proposed method employs multi-task learning that couples occupancy, feature, and spatial embeddings.

The pipeline involves two main stages: the learning stage and the clustering stage. In the learning stage, a voxelized point cloud serves as input, and through a network architecture like a 3D UNet, various point-wise predictions are made, including semantic segmentation and representations for feature and spatial embeddings. The clustering stage benefits from the integration of predicted occupancy sizes and feature embeddings, thereby enhancing clustering accuracy through an adaptive thresholding strategy. This method effectively clusters difficult samples and mitigates issues of over-segmentation.

Numerical Results

The paper presents empirical evaluations across several benchmark datasets, with notable performance improvements particularly in terms of mean Average Precision (mAP). OccuSeg achieves a remarkable 12.3 mAP gain on ScanNetV2, placing it at the forefront of current methodologies in this domain. It also delivers robust results on the S3DIS and SceneNN datasets, indicating its versatility and applicability across varied data types.

Implications and Future Directions

This paper has profound implications for the automation of 3D data processing in diverse fields such as autonomous navigation and spatial analysis in AR/VR systems. By enhancing the understanding of 3D environments through accurate instance segmentation, OccuSeg lays groundwork for more sophisticated real-time applications.

Future work may focus on optimizing these algorithms for even greater computational efficiency and scalability, potentially incorporating tailored architectures for sub-object level segmentation. Additional research could explore integrating this system with real-time 3D reconstruction for dynamic scene analysis, further bridging the gap between practical deployment and theoretical development.

In conclusion, by advancing the field of 3D instance segmentation through the introduction of occupancy measures, this paper provides both substantial empirical results and a new direction for subsequent research in 3D modeling and deep learning applications.