Context has proven to be one of the most important factors in object layout reasoning for 3D scene understanding. Existing deep contextual models either learn holistic features for context encoding or rely on pre-defined scene templates for context modeling. We argue that scene understanding benefits from object relation reasoning, which is capable of mitigating the ambiguity of 3D object detections and thus helps locate and classify the 3D objects more accurately and robustly. To achieve this, we propose a novel 3D relation module (3DRM) which reasons about object relations at pair-wise levels. The 3DRM predicts the semantic and spatial relationships between objects and extracts the object-wise relation features. We demonstrate the effects of 3DRM by plugging it into proposal-based and voting-based 3D object detection pipelines, respectively. Extensive evaluations show the effectiveness and generalization of 3DRM on 3D object detection. Our source code is available at https://github.com/lanlan96/3DRM.

在3D场景理解的对象布局推理中，上下文已被证明是最重要的因素之一。现有的深层上下文模型要么学习用于上下文编码的整体功能，要么依赖于预定义的场景模板进行上下文建模。我们认为场景理解得益于对象关系推理，它能够减轻3D对象检测的歧义，从而有助于更准确，更可靠地定位和分类3D对象。为实现此目的，我们提出了一种新颖的3D关系模块（3DRM），该模块在成对级别上介绍了对象关系。3DRM可以预测对象之间的语义和空间关系，并提取对象之间的关系特征。我们通过将3DRM分别插入基于提议和基于投票的3D对象检测管道来演示其效果。广泛的评估显示了3DRM在3D对象检测上的有效性和普遍性。我们的源代码可从https://github.com/lanlan96/3DRM获得。