统一检测驾驶场景中多种物体的骨骼结构，提升自动驾驶感知能力。
PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving
- 将不同物体类别视为独立任务，统一处理多类别骨骼检测。
- 在OpenLane数据集上实现当前最优的车道线检测性能。
- 新构建自行车骨骼数据集，验证框架对新类别的迁移能力。
物体骨骼为结构信息提供了简洁表征，捕捉姿态与朝向等关键特征，对自动驾驶至关重要。然而，仅基于输入图像同时处理多实例和多类别仍缺乏统一架构。本文提出PoseDriver，一种面向驾驶场景常见物体的底向上多类别骨骼检测统一框架。通过将每类物体建模为独立任务，系统性应对多任务学习挑战。具体地，提出基于骨骼表示的车道线检测新方法，在OpenLane数据集上达到领先性能；此外，构建了新的自行车骨骼检测数据集，并评估框架向新类别迁移的能力。实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Object skeletons offer a concise representation of structural information, capturing essential aspects of posture and orientation that are crucial for autonomous driving applications. However, a unified architecture that simultaneously handles multiple instances and categories using only the input image remains elusive. In this paper, we introduce PoseDriver, a unified framework for bottom-up multi-category skeleton detection tailored to common objects in driving scenarios. We model each category as a distinct task to systematically address the challenges of multi-task learning. Specifically, we propose a novel approach for lane detection based on skeleton representations, achieving state-of-the-art performance on the OpenLane dataset. Moreover, we present a new dataset for bicycle skeleton detection and assess the transferability of our framework to novel categories. Experimental results validate the effectiveness of the proposed approach.
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