连接视觉与运动以实现自监督的以对象为中心的感知
Linking vision and motion for self-supervised object-centric perception
AI总结：
本文提出仅以 RGB 视频和车辆位姿为输入的自监督对象中心感知模型，可融合多相机时序视角并跟踪车辆与行人。
摘要：
以对象为中心的表示使自动驾驶算法能够推理许多独立智能体与场景特征之间的交互。传统上，这些表示是通过监督学习获得的，但这会将感知与下游驾驶任务解耦，并可能损害泛化能力。在本工作中，我们改造了一个自监督的以对象为中心的视觉模型，使其仅使用 RGB 视频和车辆位姿作为输入来执行对象分解。我们证明了我们的方法在 Waymo Open 感知数据集上取得了有前景的结果。尽管对象掩码质量落后于监督方法或使用更多特权信息的替代方法，但我们发现我们的模型能够学习一种随时间融合多个相机视角的表示，并成功跟踪数据集中的许多车辆和行人。我们模型的代码可在 https://github.com/wayveai/SOCS 获取。
英文摘要：
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representations have been obtained via supervised learning, but this decouples perception from the downstream driving task and could harm generalization. In this work we adapt a self-supervised object-centric vision model to perform object decomposition using only RGB video and the pose of the vehicle as inputs. We demonstrate that our method obtains promising results on the Waymo Open perception dataset. While object mask quality lags behind supervised methods or alternatives that use more privileged information, we find that our model is capable of learning a representation that fuses multiple camera viewpoints over time and successfully tracks many vehicles and pedestrians in the dataset. Code for our model is available at https://github.com/wayveai/SOCS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
