Simple and Effective Synthesis of Indoor 3D Scenes (2204.02960v2)

Abstract: We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A vision-and-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the R2R benchmark. Our code will be made available to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks.

• The paper introduces a simplified image-to-image GAN that directly synthesizes full-resolution RGB-D indoor scenes from incomplete point clouds.
• It achieves significant performance gains, including a 27.9% improvement in FID scores on panoramic Matterport3D images compared to previous methods.
• The model enhances vision-and-language navigation by boosting success rates by up to 1.5%, demonstrating practical benefits in dynamic 3D scene generation.

Synthesis of Indoor 3D Scenes from Incomplete Inputs

The paper "Simple and Effective Synthesis of Indoor 3D Scenes" addresses the problem of generating immersive 3D indoor scenes from limited visual data, utilizing a simpler and more effective method compared to existing complex approaches. It proposes an image-to-image Generative Adversarial Network (GAN) that directly synthesizes high-resolution RGB-D images from reprojections of incomplete point clouds, significantly outperforming prior work.

Summary of Contributions

The paper critiques the complexity of existing methods for 3D scene synthesis, which often involve multiple separately trained components and stages. The proposed method eliminates the need for sophisticated auxiliary components such as semantic segmentation inputs and multi-stage training. Key contributions include:

1. Simplified Model Architecture: The proposed approach employs a straightforward image-to-image GAN architecture that maps directly from the guidance images—generated from reprojected point clouds—to full-resolution RGB-D images. This model simplifies previous architectures that required stochastic generators and normalization layers.
2. Significant Performance Improvements: The proposed model demonstrates superior performance on the Matterport3D and RealEstate10K datasets when evaluated by both humans and Fréchet Inception Distance (FID) scores. For example, on panoramic images from Matterport3D, the FID score improvements show relative enhancements over Pathdreamer by 27.9% in single-step viewpoint predictions.
3. Generative Data Augmentation: The authors explore the utility of their model in the vision-and-language navigation (VLN) domain. They demonstrate that a VLN agent trained with trajectories augmented by their model improves its success rate by up to 1.5% over state-of-the-art baselines on the R2R benchmark.

Detailed Technical Insights

The model is noted for its robustness derived from partial convolutional layers and random masking strategies during training, which enhance its ability to handle large regions with missing information. The absence of reliance on semantic segmentation inputs broadens the data range available for training, as it can leverage more diverse RGB-D datasets and video resources.

Furthermore, the paper highlights that their model has high applicability for interactive content creation, synthetic video generation, and embodied AI tasks due to its ability to predict novel views and fill out large unseen regions with high fidelity.

Theoretical and Practical Implications

The research underscores the potential to significantly streamline the process of 3D scene synthesis by reducing model complexity while achieving superior results. This simplification opens up new possibilities for broader applications of synthetic 3D environments, particularly in resource-constrained settings like mobile robotics and AR/VR applications, where computational efficiency is critical.

Looking forward, the findings suggest a promising direction for further research in image-to-image translation paradigms, potentially extending to areas where full 3D reconstructions or semantic data are not available, yet realistic scene rendering is needed. Given its practical success in enhancing VLN tasks, this approach has implications for developing advanced navigation systems that rely more heavily on generative and predictive capabilities.

Conclusion

This work contributes both a practical methodological improvement for generating 3D scenes and a compelling demonstration of its application in VLN. The simplicity coupled with high performance evidenced by this model emphasizes the potential for widescale adoption in contexts requiring dynamic and realistic scene synthesis. The provided codebase ensures accessibility and invites broader community engagement for further experimenting and augmenting diverse datasets using this method.