RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent

• URL: http://arxiv.org/abs/2307.03017v3
• Date: Mon, 27 Nov 2023 11:38:39 GMT
• Title: RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent
• Authors: Yijie Deng, Lei Han, Tianpeng Lin, Lin Li, Jinzhi Zhang, and Lu Fang
• Abstract summary: EffLiFe is a novel light field optimization method that produces high-quality light fields from sparse view images in real time. Our method achieves comparable visual quality while being 100x faster on average than state-of-the-art offline methods.
• Score: 23.4659443904092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field generation from sparse view inputs. Existing methods can be classified into offline techniques, which can generate high-quality novel views but at the cost of long inference/training time, and online methods, which either lack generalizability or produce unsatisfactory results. However, we have observed that the intrinsic sparse manifold of Multi-plane Images (MPI) enables a significant acceleration of light field generation while maintaining rendering quality. Based on this insight, we introduce EffLiFe, a novel light field optimization method, which leverages the proposed Hierarchical Sparse Gradient Descent (HSGD) to produce high-quality light fields from sparse view images in real time. Technically, the coarse MPI of a scene is first generated using a 3D CNN, and it is further sparsely optimized by focusing only on important MPI gradients in a few iterations. Nevertheless, relying solely on optimization can lead to artifacts at occlusion boundaries. Therefore, we propose an occlusion-aware iterative refinement module that removes visual artifacts in occluded regions by iteratively filtering the input. Extensive experiments demonstrate that our method achieves comparable visual quality while being 100x faster on average than state-of-the-art offline methods and delivering better performance (about 2 dB higher in PSNR) compared to other online approaches.

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