DeepStereo: Learning to Predict New Views from the World's Imagery (1506.06825v1)

Abstract: Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision, but their use in graphics problems has been limited. In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to traditional approaches which consist of multiple complex stages of processing, each of which require careful tuning and can fail in unexpected ways, our system is trained end-to-end. The pixels from neighboring views of a scene are presented to the network which then directly produces the pixels of the unseen view. The benefits of our approach include generality (we only require posed image sets and can easily apply our method to different domains), and high quality results on traditionally difficult scenes. We believe this is due to the end-to-end nature of our system which is able to plausibly generate pixels according to color, depth, and texture priors learnt automatically from the training data. To verify our method we show that it can convincingly reproduce known test views from nearby imagery. Additionally we show images rendered from novel viewpoints. To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.

• The paper introduces an end-to-end deep network that predicts novel views from extensive Street View imagery.
• It features a dual architecture where a selection tower estimates depth probabilities and a color tower refines pixel colors.
• The method outperforms traditional pipelines in challenging scenes, indicating potential for real-time image synthesis.

Analysis of "DeepStereo: Learning to Predict New Views from the World's Imagery"

The paper "DeepStereo: Learning to Predict New Views from the World's Imagery" innovatively addresses the problem of new view synthesis using deep learning techniques. This approach stands as a significant contribution to the fields of computer vision and computer graphics, particularly in the field of image-based rendering (IBR).

Methodology

The authors propose a novel deep network architecture that predicts pixel values for novel viewpoints directly from posed image sets. The network employs an end-to-end training approach, leveraging the extensive data available from Google's Street View. The system's fundamental innovation lies in its ability to synthesize unseen views without relying on complex and error-prone traditional multi-stage processing pipelines.

Network Architecture

The network architecture distinguishes itself through its separation into two primary components: the selection tower and the color tower.

• Selection Tower: This part of the network estimates the likelihood of each pixel being at a given depth across different planes. Through layers of convolution, the network learns how to compute an optimal probability map over depth, which guides pixel selection from the input volumes.
• Color Tower: This computes the output color for each pixel, leveraging the learned depths to combine and interpolate pixel values from input images.

The architecture operates on plane-sweep volumes, enabling the efficient alignment of input image pixels across different depths. The transformation into plane sweep volumes mitigates the need for pose parameters as explicit inputs, simplifying the learning task into one of optimization using deep learning.

Results

The results indicate that the DeepStereo model can convincingly reproduce known test views, outperforming traditional approaches in challenging scenes. The model handles a variety of surfaces, including trees and glass, with resilience to typical rendering artifacts like tearing and mismatches around occlusions. While the paper notes some limitations in speed and flexibility concerning the number of input views, the qualitative comparisons show that the method is competitive with existing state-of-the-art solutions.

Implications and Future Work

The implications of this research extend broadly into applications such as virtual reality, cinematography, and teleconferencing. With further refinements, including potential adaptations for real-time performance on GPUs, DeepStereo could influence real-time rendering applications. Additionally, the architecture might be expanded to work with varying numbers of input images, adapting seamlessly to dynamic input conditions.

Further research could explore the adoption of recurrent network models to handle varying input sizes or the potential training on a wider variety of data to enhance generalization. These advancements could lead to implementations in video frame synthesis and potentially integrate with traditional stereo modeling frameworks.

Conclusion

In conclusion, DeepStereo marks a significant stride in applying deep networks to view synthesis. By embracing an end-to-end approach that intelligently combines depth prediction with color rendering, the authors present a compelling case for deep learning's expanding role in computer graphics. This paper serves as a foundation for future exploration into increasingly sophisticated deep learning models capable of providing high-quality synthesized views across diverse and challenging environments.