SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans (2003.12622v1)

Abstract: We present a novel approach to reconstructing lightweight, CAD-based representations of scanned 3D environments from commodity RGB-D sensors. Our key idea is to jointly optimize for both CAD model alignments as well as layout estimations of the scanned scene, explicitly modeling inter-relationships between objects-to-objects and objects-to-layout. Since object arrangement and scene layout are intrinsically coupled, we show that treating the problem jointly significantly helps to produce globally-consistent representations of a scene. Object CAD models are aligned to the scene by establishing dense correspondences between geometry, and we introduce a hierarchical layout prediction approach to estimate layout planes from corners and edges of the scene.To this end, we propose a message-passing graph neural network to model the inter-relationships between objects and layout, guiding generation of a globally object alignment in a scene. By considering the global scene layout, we achieve significantly improved CAD alignments compared to state-of-the-art methods, improving from 41.83% to 58.41% alignment accuracy on SUNCG and from 50.05% to 61.24% on ScanNet, respectively. The resulting CAD-based representations makes our method well-suited for applications in content creation such as augmented- or virtual reality.

• The paper introduces a novel joint optimization framework using a graph neural network to align CAD models with RGB-D scan data.
• It achieves significant improvements in CAD alignment accuracy, boosting performance on SUNCG from 41.83% to 58.41% and on ScanNet from 50.05% to 61.24%.
• The approach offers practical benefits for VR, AR, and robotics by providing globally consistent, lightweight 3D reconstructions from noisy scan data.

Overview of SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans

The paper presents a novel methodology known as SceneCAD, which focuses on reconstructing lightweight CAD-based representations of scanned 3D environments utilizing commodity RGB-D sensors. The authors propose a joint optimization approach to align CAD models and estimate scene layouts by modeling the relationships between objects and layout components using a graph neural network. This method aims to produce globally consistent representations by leveraging the intrinsic coupling between object arrangement and scene layout.

Methodology

SceneCAD introduces a comprehensive pipeline for creating CAD-based scene representations from RGB-D scans. The approach begins by detecting objects and layout elements within the input scan and then finds suitable CAD models from a predefined candidate pool. Each component detected forms a node within a message-passing graph neural network, which estimates both object-object and object-layout relationships.

The object CAD models are aligned to the scene by establishing dense geometric correspondences, and the scene layout is predicted hierarchically starting from corners and edges to final layout planes. This pipeline aims for a globally consistent alignment and robust retrieval, resulting in lightweight CAD representations.

Numerical Achievements and Comparisons

The effectiveness of SceneCAD is demonstrated through strong numerical results on both synthetic and real-world datasets, SUNCG and ScanNet. The CAD alignment accuracy improves significantly from 41.83% to 58.41% on SUNCG and from 50.05% to 61.24% on ScanNet. These results underscore the advantage of incorporating object-layout relationships into the alignment process, outperforming several state-of-the-art geometric feature matching approaches and learned feature methodologies.

Implications and Future Directions

The implications of SceneCAD extend to multiple domains such as virtual reality (VR), augmented reality (AR), and robotics. The CAD-based representations enable more efficient content creation with enhanced global consistency of reconstructions. The method's ability to produce clean and lightweight models from incomplete and noisy data addresses a long-standing gap between scanned data and artist-modeled content.

Future research can build upon SceneCAD by exploring more sophisticated graph neural network architectures for relational inference, potentially improving the robustness of relationship predictions across a wider range of scenarios. Additionally, enhancing the realism of the reconstructions with texture mapping could be a valuable direction for creating more immersive VR/AR environments.

In conclusion, SceneCAD presents a transformative approach to 3D scene reconstruction by addressing the inherent coupling between objects and layouts. The paper's contributions, particularly the hierarchical layout prediction and graph-based relational modeling, pave the way for further advancements in the field of 3D reconstruction and scene understanding.