Instance shape reconstruction from a 3D scene involves recovering the full
geometries of multiple objects at the semantic instance level. Many methods
leverage data-driven learning due to the intricacies of scene complexity and
significant indoor occlusions. Training these methods often requires a
large-scale, high-quality dataset with aligned and paired shape annotations
with real-world scans. Existing datasets are either synthetic or misaligned,
restricting the performance of data-driven methods on real data. To this end,
we introduce LASA, a Large-scale Aligned Shape Annotation Dataset comprising
10,412 high-quality CAD annotations aligned with 920 real-world scene scans
from ArkitScenes, created manually by professional artists. On this top, we
propose a novel Diffusion-based Cross-Modal Shape Reconstruction (DisCo)
method. It is empowered by a hybrid feature aggregation design to fuse
multi-modal inputs and recover high-fidelity object geometries. Besides, we
present an Occupancy-Guided 3D Object Detection (OccGOD) method and demonstrate
that our shape annotations provide scene occupancy clues that can further
improve 3D object detection. Supported by LASA, extensive experiments show that
our methods achieve state-of-the-art performance in both instance-level scene
reconstruction and 3D object detection tasks.

从 3D 场景中实例形状重建涉及恢复语义实例级别的多个对象的完整几何形状。我们介绍了一个大规模对齐形状注释数据集 LASA，其中包括对齐 920 个现实场景扫描的 10,412 个高质量 CAD 注释。我们提出了一种新的基于扩散的跨模态形状重建方法（DisCo），它通过混合特征聚合设计来融合多模态输入并恢复高保真度的对象几何形状。此外，我们还提出了一种基于占据的 3D 对象检测方法（OccGOD），并展示了我们的形状注释提供的场景占据线索可以进一步提高 3D 对象检测。通过 LASA 的支持，大量实验证明我们的方法在实例级别场景重建和 3D 对象检测任务中实现了最先进的性能。