DINO-SD: ICRA 2024 RoboDepthチャレンジ優勝ソリューション
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
追加データなしで周囲視点の深度推定を高精度かつ頑健に行うDINO-SDを提案し、ICRA 2024 RoboDepthチャレンジのトラック4で最高性能を達成した。
著者: Yifan Mao, Ming Li, Jian Liu, Jiayang Liu, Zihan Qin, Chunxi Chu, Jialei Xu, Wenbo Zhao, Junjun Jiang, Xianming Liu
分類: cs.CV, cs.RO
原文アブストラクト
Surround-view depth estimation is a crucial task aims to acquire the depth maps of the surrounding views. It has many applications in real world scenarios such as autonomous driving, AR/VR and 3D reconstruction, etc. However, given that most of the data in the autonomous driving dataset is collected in daytime scenarios, this leads to poor depth model performance in the face of out-of-distribution(OoD) data. While some works try to improve the robustness of depth model under OoD data, these methods either require additional training data or lake generalizability. In this report, we introduce the DINO-SD, a novel surround-view depth estimation model. Our DINO-SD does not need additional data and has strong robustness. Our DINO-SD get the best performance in the track4 of ICRA 2024 RoboDepth Challenge.
