This paper proposes a shape anchor guided learning strategy (AncLearn) for
robust holistic indoor scene understanding. We observe that the search space
constructed by current methods for proposal feature grouping and instance point
sampling often introduces massive noise to instance detection and mesh
reconstruction. Accordingly, we develop AncLearn to generate anchors that
dynamically fit instance surfaces to (i) unmix noise and target-related
features for offering reliable proposals at the detection stage, and (ii)
reduce outliers in object point sampling for directly providing well-structured
geometry priors without segmentation during reconstruction. We embed AncLearn
into a reconstruction-from-detection learning system (AncRec) to generate
high-quality semantic scene models in a purely instance-oriented manner.
Experiments conducted on the challenging ScanNetv2 dataset demonstrate that our
shape anchor-based method consistently achieves state-of-the-art performance in
terms of 3D object detection, layout estimation, and shape reconstruction. The
code will be available at this https URL

该论文提出了一种形状锚点引导学习策略（AncLearn）来实现强大的室内场景理解。通过在检测阶段为生成可靠的提案提供可靠的特征，并在重建过程中直接提供具有良好结构的几何先验来降低噪声和离群点，将 AncLearn 嵌入到一种纯实例导向的重建学习系统（AncRec）中，从而以纯粹的实例为导向，生成高质量的语义场景模型。在具有挑战性的 ScanNetv2 数据集上进行的实验证明，基于形状锚点的方法在 3D 目标检测、布局估计和形状重建方面始终达到最先进的性能。