Neighborhood Spatial Aggregation based Efficient Uncertainty Estimation for Point Cloud Semantic Segmentation

Uncertainty estimation for point cloud semantic segmentation is to quantify the confidence degree for the predicted label of points, which is essential for decision-making tasks. This paper proposes a neighborhood spatial aggregation based method, NSA-MC dropout, to achieve efficient uncertainty estimation for point cloud semantic segmentation. Unlike the traditional uncertainty estimation method MC dropout de-pending on repeated inferences, our NSA-MC dropout achieves uncertainty estimation through one-time inference. Specifically, a space-dependent method is designed to sample the model many times by performing stochastic forward pass through the model just once, and it approximates the repeated inferences based sampling process in MC dropout. Besides, a neighborhood spatial aggregation module, called NSA, aggregates neighborhood probabilistic outputs for each point and works with space-dependent sampling to establish output distribution. Finally, we propose an uncertainty-aware framework NSA-MC dropout to capture the uncertainty of prediction results efficiently. Experimental results show that our method obtains comparable performance with MC dropout. More significantly, our NSA-MC dropout has little influence on the efficiency of semantic inference. It is much faster than MC dropout, and the inference time does not establish a coupling relation with the sampling times. Our code is available at https://github.com/chaoqi7/Uncertainty_Estimation_PCSS

Authors

• Chao Qi
• Jianqin Yin
• Huaping Liu 0001
• Jun Liu 0007

Keywords

• Visualization
• Uncertainty
• Design methodology
• Semantics
• Decision making
• Estimation
• Stochastic processes
• Uncertainty Estimation
• Point Cloud
• Semantic Segmentation
• Point Cloud Semantic Segmentation
• Efficient Uncertainty Estimation
• Sampling Time
• Inference Time
• Output Distribution
• Neural Network
• Large-scale Datasets
• Multilayer Perceptron
• Gaussian Process
• Baseline Methods
• Network Weights
• Path Planning
• Neighboring Points
• Adjacent Points
• Dropout Layer
• Acquisition Function
• Increase In Uncertainty
• Bayesian Neural Network
• Decoding Stage
• Video Segments