SynSin: End-to-end View Synthesis from a Single Image (1912.08804v2)

Abstract: Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D scene from a single image. As a result, current methods typically use multiple images, train on ground-truth depth, or are limited to synthetic data. We propose a novel end-to-end model for this task; it is trained on real images without any ground-truth 3D information. To this end, we introduce a novel differentiable point cloud renderer that is used to transform a latent 3D point cloud of features into the target view. The projected features are decoded by our refinement network to inpaint missing regions and generate a realistic output image. The 3D component inside of our generative model allows for interpretable manipulation of the latent feature space at test time, e.g. we can animate trajectories from a single image. Unlike prior work, we can generate high resolution images and generalise to other input resolutions. We outperform baselines and prior work on the Matterport, Replica, and RealEstate10K datasets.

• The paper introduces a novel end-to-end method for view synthesis from a single RGB image using a differentiable neural point cloud renderer and a generative refinement network.
• It demonstrates superior performance over traditional voxel-based and image translation approaches on real-world datasets like Matterport3D and RealEstate10K.
• The approach offers significant implications for VR, image editing, and autonomous navigation by enhancing scene understanding and filling occlusions.

An Overview of "SynSin: End-to-end View Synthesis from a Single Image"

The paper "SynSin: End-to-end View Synthesis from a Single Image" introduces an approach for generating novel views of a scene from a single RGB image without relying on ground-truth 3D information. The authors tackle the complex challenge of view synthesis, which traditionally requires multiple images or 3D data, by proposing a novel methodology that integrates a differentiable rendering mechanism with a generative model. This approach is presented as a step forward in handling real-world scenarios over synthetic datasets typically employed in past research.

Methodology

SynSin achieves view synthesis through two key components: the neural point cloud renderer and the generative refinement network. The renderer employs a high-resolution point cloud representation of the scene's features predicted from an input image. Unlike conventional rendering pipelines that focus on RGB values, SynSin transforms the point cloud into meaningful features using a differentiable process. This flexibility allows SynSin to adapt to different image resolutions and domains.

A refinement network further improves the synthesis by utilizing adversarial training with discriminators to refine features and fill in occlusions or holes, which are inherent challenges when generating new views from a single perspective. This moves beyond simple depth estimation, aiming at comprehensive scene understanding and semantic inpainting.

Experimental Evaluation

The evaluation encompasses three real-world datasets: Matterport3D, RealEstate10K, and Replica, which include variations in scene complexity and structure. SynSin demonstrates superior performance against baselines such as voxel-based methods and image-to-image translations, both quantitatively, through metrics like PSNR, SSIM, and perceptual similarity, and qualitatively, in generating visually coherent images even with significant transformations.

When compared with systems that utilize multi-view supervision or ground truth depths, SynSin achieves competitive outcomes, underscoring the efficacy of its point cloud-based system even without explicit depth supervision. Importantly, the method shows strong generalization capabilities under a variety of conditions and unprecedented input resolutions.

Implications and Future Directions

The implications of SynSin are significant for the fields of computer vision and 3D image synthesis. By enabling view synthesis from a single image, SynSin presents opportunities for applications in image editing, virtual reality, and animation. The use of differentiable rendering in point cloud representation could inspire further integration of geometric and semantic understanding in other domains such as object recognition or autonomous navigation.

Future research could expand on the scalability of SynSin to more diverse environments and scenes, exploring how its architecture can be adapted to dynamic changes in viewpoint or lighting conditions. Additionally, further optimization of the training process or refinement network could enhance the model's efficiency and accuracy.

Overall, SynSin represents a meaningful contribution that blends novel rendering techniques with generative modeling, demonstrating the potential to overcome longstanding challenges in view synthesis from minimal input data.