クロス点埋め込みによる3D密予測の不確かさ推定
Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings
3D点群の密予測タスクにおいて、点間の埋め込み関係を活用して不確かさを推定する手法CUEとCUE+を提案し、幾何特徴学習と意味セグメンテーションで有効性を示した。
著者: Kaiwen Cai, Chris Xiaoxuan Lu, Xiaowei Huang
分類: cs.RO, cs.CV
原文アブストラクト
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.
