论文标题
通过未来的对象检测从LIDAR预测
Forecasting from LiDAR via Future Object Detection
论文作者
论文摘要
对象检测和预测是体现感知的基本组成部分。但是，这两个问题在很大程度上被社区孤立地研究。在本文中，我们提出了一种基于原始传感器测量而不是地面真相轨迹的端到端检测和运动预测的方法。我们直接预测将来的对象位置和背景，以确定每个轨迹的开始，而不是预测当前的帧位置并预测到时间。与其他模块化或端到端基线相比，我们的方法不仅提高了整体准确性，而且还促使我们重新考虑显式跟踪对体现感知的作用。此外，通过以多对一的方式将未来和当前位置联系起来，我们的方法能够推理多个未来，这是端到端方法以前被认为很难的能力。我们对流行的Nuscenes数据集进行了广泛的实验，并证明了我们方法的经验有效性。此外，我们研究了端到端设置的重用标准预测指标的适当性，并找到了许多限制，使我们能够构建简单的基线来游戏这些指标。我们使用一组新型的联合预测和检测指标来解决这个问题，这些预测和检测指标将常用的AP指标从检测界扩展到测量预测准确性。我们的代码可在https://github.com/neeharperi/futuredet上找到
Object detection and forecasting are fundamental components of embodied perception. These two problems, however, are largely studied in isolation by the community. In this paper, we propose an end-to-end approach for detection and motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Instead of predicting the current frame locations and forecasting forward in time, we directly predict future object locations and backcast to determine where each trajectory began. Our approach not only improves overall accuracy compared to other modular or end-to-end baselines, it also prompts us to rethink the role of explicit tracking for embodied perception. Additionally, by linking future and current locations in a many-to-one manner, our approach is able to reason about multiple futures, a capability that was previously considered difficult for end-to-end approaches. We conduct extensive experiments on the popular nuScenes dataset and demonstrate the empirical effectiveness of our approach. In addition, we investigate the appropriateness of reusing standard forecasting metrics for an end-to-end setup, and find a number of limitations which allow us to build simple baselines to game these metrics. We address this issue with a novel set of joint forecasting and detection metrics that extend the commonly used AP metrics from the detection community to measuring forecasting accuracy. Our code is available at https://github.com/neeharperi/FutureDet
