论文标题
通过交叉点嵌入3D密集预测的不确定性估计
Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings
论文作者
论文摘要
密集的预测任务对于3D点云很常见，但是在大量点及其嵌入中固有的不确定性长期以来一直被忽略。在这项工作中，我们提出了提示，这是一种新型的不确定性估计方法，用于3D点云中的密集预测任务。受公制学习的启发，提示的关键思想是探索传统的3D密集预测管道上的交叉点嵌入。具体而言，提示涉及建立概率嵌入模型，然后在嵌入空间中强制执行大量点的指标。我们还提出了CUE+，该CUE+通过对协方差矩阵中的跨点依赖性进行显式建模来增强提示。我们证明，CUE和CUE+对于具有两个不同任务的3D点云中的不确定性估计都是通用且有效的：（1）在3D几何特征学习中，我们首次获得了良好的校准不确定性，（2）在语义段中，我们减少了不确定性的预期校准误差，使您的预期校准误差提高了16.5％。所有不确定性均可估算，而不会损害预测性能。
Dense prediction tasks are common for 3D point clouds, but the uncertainties inherent in massive points and their embeddings have long been ignored. In this work, we present CUE, a novel uncertainty estimation method for dense prediction tasks in 3D point clouds. Inspired by metric learning, the key idea of CUE is to explore cross-point embeddings upon a conventional 3D dense prediction pipeline. Specifically, CUE involves building a probabilistic embedding model and then enforcing metric alignments of massive points in the embedding space. We also propose CUE+, which enhances CUE by explicitly modeling crosspoint dependencies in the covariance matrix. We demonstrate that both CUE and CUE+ are generic and effective for uncertainty estimation in 3D point clouds with two different tasks: (1) in 3D geometric feature learning we for the first time obtain wellcalibrated uncertainty, and (2) in semantic segmentation we reduce uncertainty's Expected Calibration Error of the state-of-the-arts by 16.5%. All uncertainties are estimated without compromising predictive performance.
