VISC: 普及型視覚慣性センサによるmmWaveレーダのシーンフロー推定
VISC: mmWave Radar Scene Flow Estimation using Pervasive Visual-Inertial Supervision
高価なLiDARの代わりに普及している視覚慣性センサのデータを教師信号として、mmWaveレーダのシーンフロー推定を学習するフレームワークを提案。煙環境でもLiDARを用いる最先端手法を上回る性能を実現した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Kezhong Liu, Yiwen Zhou, Mozi Chen, Jianhua He, Jingao Xu, Zheng Yang, Chris Xiaoxuan Lu, Shengkai Zhang
分類: cs.CV, cs.RO
原文アブストラクト
This work proposes a mmWave radar's scene flow estimation framework supervised by data from a widespread visual-inertial (VI) sensor suite, allowing crowdsourced training data from smart vehicles. Current scene flow estimation methods for mmWave radar are typically supervised by dense point clouds from 3D LiDARs, which are expensive and not widely available in smart vehicles. While VI data are more accessible, visual images alone cannot capture the 3D motions of moving objects, making it difficult to supervise their scene flow. Moreover, the temporal drift of VI rigid transformation also degenerates the scene flow estimation of static points. To address these challenges, we propose a drift-free rigid transformation estimator that fuses kinematic model-based ego-motions with neural network-learned results. It provides strong supervision signals to radar-based rigid transformation and infers the scene flow of static points. Then, we develop an optical-mmWave supervision extraction module that extracts the supervision signals of radar rigid transformation and scene flow. It strengthens the supervision by learning the scene flow of dynamic points with the joint constraints of optical and mmWave radar measurements. Extensive experiments demonstrate that, in smoke-filled environments, our method even outperforms state-of-the-art (SOTA) approaches using costly LiDARs.
