Learning to Synthesize a 4D RGBD Light Field from a Single Image (1708.03292v1)

Abstract: We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point. Please see our supplementary video at https://youtu.be/yLCvWoQLnms

• The paper introduces a CNN-based approach that synthesizes a 4D RGBD light field from a single image using unsupervised depth estimation and novel depth consistency regularization.
• It presents an extensive light field dataset captured with a Lytro Illum camera, achieving lower mean L1 error compared to existing baseline methods.
• The framework paves the way for practical advances in virtual and augmented reality by simplifying scene capture and enhancing unsupervised geometry learning.

Overview of "Learning to Synthesize a 4D RGBD Light Field from a Single Image"

The paper "Learning to Synthesize a 4D RGBD Light Field from a Single Image" addresses the challenge of generating a 4D RGBD (color and depth) light field from a single 2D image input. The authors present a machine learning framework leveraging a convolutional neural network (CNN) that performs unsupervised depth estimation and view synthesis to achieve this task. The proposed method is not only conceptualized for producing an RGBD representation but also enhances the depth prediction for scene geometry.

The central contributions of this paper include the development of an extensive light field dataset, the formulation of a novel depth consistency regularization, and the design of an end-to-end trainable network to generate light fields from a single image input. Foremost, the dataset amassed by the authors consists of 3343 light fields from scenes featuring flowers and plants, captured using a Lytro Illum plenoptic camera. This dataset serves as a cornerstone for the supervised learning of the network aimed at generating synthetic light fields.

The proposed architecture consists of two primary CNNs. The first network is responsible for estimating the scene geometry by predicting a depth map for every viewpoint in the light field. This stage is driven by a physically-grounded consistency regularization which encourages the predicted ray depths to remain consistent across different views, effectively mitigating common errors in texture-less regions and occluded areas. This regularization is novel in that it enforces light field depth coherence, which is not a typical feature of prior view synthesis methods.

The second CNN processes the initial Lambertian rendering, augmenting it with corrections for occlusions and non-Lambertian effects—a critical step given the ill-posed nature of reconstructing occluded and reflective areas from a single viewpoint.

Quantitative evaluation within the paper demonstrates that their method maintains a lower mean $L_1$ error in predicting light fields compared to existing techniques like appearance flow, showcasing improvements even without additional geometric representations. Despite the model's reliance on a limited scene category for training, it exhibits encouraging generalization capabilities, extending to other complex scene types with intricate depth structures.

The implications of synthesizing 4D light fields from a single image are significant. In practice, this method points toward more accessible capture setups for applications in virtual and augmented reality, where capturing extensive and detailed geometrical data from limited view samples is often a challenge. Theoretically, this work contributes a valuable advancement in unsupervised geometry learning from lightweight image data, with the potential adaptation to other geometric forms like meshes or point clouds.

Looking forward, additional opportunity lies in expanding the dataset to encompass more heterogeneous scenes, which could bolster the model's effectiveness across a broader spectrum of environments. Furthermore, experimenting with alternative 3D representations could enhance the fine-grained detail achievable in the synthesized light fields, thus mimicking real-world visuals with greater fidelity.

The proposed framework and dataset represent a proactive step towards redefining view synthesis tasks and broadening the possibilities in immersive content creation, aligning the capability of machine learning with real-world visual perception challenges.