IndoorBEV：在室内场景中通过基于掩码的预测实现鸟瞰视角感知的目标联合检测与足迹补全
IndoorBEV: Joint Detection and Footprint Completion of Objects via Mask-based Prediction in Indoor Scenarios for Bird's-Eye View Perception
AI总结：
本文提出 IndoorBEV，一种基于BEV掩码预测的室内机器人感知方法，通过联合输出类别与实例掩码来检测并补全静态、动态目标足迹。
摘要：
在复杂室内3D点云中检测多样目标给机器人感知带来了重大挑战，尤其是在目标形状各异、场景杂乱以及静态与动态元素共存的情况下，传统边界框方法会失效。为解决这些局限，我们提出了 IndoorBEV，这是一种面向室内移动机器人的新型基于掩码的鸟瞰视角（BEV）方法。在BEV方法中，3D场景被投影到2D BEV网格中，该网格能够自然地处理遮挡，并提供一致的俯视视角，有助于区分静态障碍物与动态智能体。所得2D BEV结果可直接用于导航、运动预测和规划等下游机器人任务。我们的架构利用轴紧凑编码器和基于窗口的骨干网络，从该BEV地图中提取丰富的空间特征。随后，基于查询的解码头采用学习到的目标查询，在BEV空间中同时预测目标类别和实例掩码。这种以掩码为中心的建模范式能够有效捕捉静态与动态目标的足迹，而不受其形状影响，为边界框回归提供了一种鲁棒的替代方案。我们在一个自定义室内数据集上展示了 IndoorBEV 的有效性，该数据集包含多样目标类别，包括静态物体以及机器人和杂项物品等动态元素，展现了其实现鲁棒室内场景理解的潜力。
英文摘要：
Detecting diverse objects within complex indoor 3D point clouds presents significant challenges for robotic perception, particularly with varied object shapes, clutter, and the co-existence of static and dynamic elements where traditional bounding box methods falter. To address these limitations, we propose IndoorBEV, a novel mask-based Bird's-Eye View (BEV) method for indoor mobile robots. In a BEV method, a 3D scene is projected into a 2D BEV grid which handles naturally occlusions and provides a consistent top-down view aiding to distinguish static obstacles from dynamic agents. The obtained 2D BEV results is directly usable to downstream robotic tasks like navigation, motion prediction, and planning. Our architecture utilizes an axis compact encoder and a window-based backbone to extract rich spatial features from this BEV map. A query-based decoder head then employs learned object queries to concurrently predict object classes and instance masks in the BEV space. This mask-centric formulation effectively captures the footprint of both static and dynamic objects regardless of their shape, offering a robust alternative to bounding box regression. We demonstrate the effectiveness of IndoorBEV on a custom indoor dataset featuring diverse object classes including static objects and dynamic elements like robots and miscellaneous items, showcasing its potential for robust indoor scene understanding.
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。
