Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image (2002.12212v1)

Abstract: Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction. Existing works either address one part of this problem or focus on independent objects. In this paper, we bridge the gap between understanding and reconstruction, and propose an end-to-end solution to jointly reconstruct room layout, object bounding boxes and meshes from a single image. Instead of separately resolving scene understanding and object reconstruction, our method builds upon a holistic scene context and proposes a coarse-to-fine hierarchy with three components: 1. room layout with camera pose; 2. 3D object bounding boxes; 3. object meshes. We argue that understanding the context of each component can assist the task of parsing the others, which enables joint understanding and reconstruction. The experiments on the SUN RGB-D and Pix3D datasets demonstrate that our method consistently outperforms existing methods in indoor layout estimation, 3D object detection and mesh reconstruction.

Total3DUnderstanding: A Comprehensive Approach to Indoor Scene Reconstruction

The paper "Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image" addresses a pivotal challenge in computer vision and 3D reconstruction—the holistic understanding and reconstruction of 3D indoor scenes from a single RGB image. The approach integrates scene understanding, including layout estimation and object position detection, with detailed mesh reconstruction. This integration facilitates a more comprehensive understanding of spatial environments, crucial for applications in fields such as interior design and augmented reality.

The authors propose an end-to-end solution comprising three main components: room layout and camera pose estimation, 3D object detection, and object mesh reconstruction. The novelty lies in the joint learning approach, which exploits the interdependencies between these components to enhance overall reconstruction accuracy. The hierarchical network structure follows a coarse-to-fine paradigm, where the outputs from one stage inform the next, thereby ensuring consistency and contextual coherence across the scene reconstruction process.

Methodology Overview

1. Room Layout and Camera Pose Estimation: This component predicts the global spatial configuration, involving camera orientation and room dimensions. The layout estimation leverages a deep neural network to ascertain the room's bounding box and camera pose. The approach builds on prior methods but incorporates joint training to refine predictions by leveraging object and mesh information.
2. 3D Object Detection: Utilizing an object detection network (ODN), the method predicts 3D bounding boxes for objects detected in the image. The methodology includes a relational feature encoding, considering the multilateral spatial relations between objects, which enhances the accuracy of object placement and orientation within the 3D space.
3. Mesh Reconstruction: The Mesh Generation Network (MGN) transforms detected 2D objects into detailed 3D meshes. The network adopts a novel topology modification strategy based on local density, contrasting with previous approaches that utilized fixed distance thresholds. This density-based approach ensures adaptive mesh topology changes, improving robustness across diverse object geometries.

Experimental Results and Implications

The method was evaluated on the SUN RGB-D and Pix3D datasets, demonstrating superior performance in layout estimation, 3D object detection, and mesh reconstruction compared to existing methods. Notably, the network achieved state-of-the-art results in layout prediction, indicating the efficacy of the joint learning paradigm.

Layout and Detection: The paper reports a significant improvement in 3D Intersection over Union (IoU) for layout estimation, along with enhanced average precision (AP) in 3D object detection tasks. These improvements are attributed to the cooperative loss techniques that ensure consistency between object positioning and overall scene structure.

Mesh Reconstruction: On Pix3D, the mesh generation network's adaptability to varying scales and complexities of objects was validated, outperforming benchmarks in terms of Chamfer distance—a standard metric for mesh accuracy. The local density-based topology adjustment was highlighted as a crucial factor in achieving these results.

Future Directions

The research establishes a solid foundation for further exploring integrative scene understanding techniques. Future work could address limitations such as the requirement for high-quality point clouds in training by exploring unsupervised or weakly supervised learning paradigms. Additionally, expanding the framework to accommodate dynamic and outdoor environments could broaden its application scope.

In conclusion, the paper advances the field of 3D scene understanding by delivering an integrative framework that jointly considers layout, object pose, and mesh reconstruction. The results underscore the value of holistic approaches in realizing accurate and efficient scene reconstruction from monocular images, charting a promising path for future explorations in automated 3D modeling and spatial analysis.