Gen3DSR: Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single View (2404.03421v2)

Abstract: Single-view 3D reconstruction is currently approached from two dominant perspectives: reconstruction of scenes with limited diversity using 3D data supervision or reconstruction of diverse singular objects using large image priors. However, real-world scenarios are far more complex and exceed the capabilities of these methods. We therefore propose a hybrid method following a divide-and-conquer strategy. We first process the scene holistically, extracting depth and semantic information, and then leverage an object-level method for the detailed reconstruction of individual components. By splitting the problem into simpler tasks, our system is able to generalize to various types of scenes without retraining or fine-tuning. We purposely design our pipeline to be highly modular with independent, self-contained modules, to avoid the need for end-to-end training of the whole system. This enables the pipeline to naturally improve as future methods can replace the individual modules. We demonstrate the reconstruction performance of our approach on both synthetic and real-world scenes, comparing favorable against prior works. Project page: https://andreeadogaru.github.io/Gen3DSR

• The paper introduces a modular divide-and-conquer approach which reconstructs 3D scenes from one view using distinct sub-tasks like depth estimation and instance detection.
• It leverages holistic scene parsing followed by focused object reconstruction to handle complex real-world scenes with improved fidelity.
• Competitive performance is achieved with metrics such as Chamfer Distance and F-Score, highlighting its adaptability over traditional end-to-end models.

Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single View

The paper "Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single View" by Dogaru, Özer, and Egger investigates the challenging problem of reconstructing 3D scenes from a single viewpoint, which traditionally has been approached with varying success depending on the specific application context, such as face or hair modeling. While substantial progress has been made in reconstructing single objects from a single view, scene-level complexity introduces compounded challenges, especially due to real-world complexity and diversity.

The authors present a hybrid and modular method that embraces a divide-and-conquer strategy, designed to tackle the reconstruction of complex real-world scenes by processing scenes holistically before applying a focus to objects of interest. This is contrasted against existing methodologies which either narrowly focus on specific types of objects or scenes and require dense 3D supervision or leverage large image datasets with diverse prior information.

Methodology

The proposed method engages with the input RGB image by initially parsing it to extract holistic depth and semantic information, before isolating object details for reconstruction via a modular compositional framework. It leverages several high-performing models for specific sub-tasks rather than relying on an end-to-end trainable network. This pipeline is built to be adaptable and improvable, enabling replacement or enhancement of individual modular components as new models become available. This aspect is crucial for continued improvement in performance over time.

Sub-problems identified include camera calibration, depth map prediction, segmentation of entities, instance detection for occlusion handling, and individual background and object instance reconstruction. For instance completion, the authors employ amodal completion techniques to extrapolate unseen parts of objects, thus enhancing the realism and fidelity of reconstructed objects. The system ingeniously utilizes per-object reconstructions merged into an output scene with a guided approach employing depth estimation.

Results and Comparison

The paper provides a comprehensive evaluation of the proposed methodology using synthetic (3D-FRONT and HOPE-Image datasets) and real-world datasets, demonstrating generalization capabilities and high-quality reconstructions. Against various baseline methods, the proposed solution yields favorable performance without requiring 3D-supervision. Notably, it achieves competitive outcomes in terms of Chamfer Distance and F-Score metrics, significantly outperforming some end-to-end trained models when generalized to unseen domains and datasets.

Contributions and Implications

The research noted strong contributions in building an adaptable compositional framework that circumvents the need for end-to-end training, highlighting its practical applicability and potential for expansion with newer sub-task advancements. Although pipeline intertwining inherently inherits limitations when combining missteps at each stage (camera, depth, or object estimation), the modular nature allows for individuated spotlight improvements. Future developments in related fields such as improved depth estimation through leveraging scene context or refined object completion via diffusion models could further enhance the framework's capabilities.

Conclusion

In conclusion, this paper addresses the complex problem of reconstructing full 3D scenes from single images with an innovative modular divide-and-conquer strategy. This approach not only showcases strong foundational results but also sets forth a robust template upon which future models and techniques can build. The emphasis on modularity and adaptability positions this methodology favorably within the landscape of evolving computer vision and 3D graphics capabilities, specifically for applications needing versatile, high-quality scene reconstructions. As 3D reconstruction continues to push towards more complex and real-world applicable solutions, the insights and results from this paper provide a strong technical basis upon which further innovation can be developed.