Neural Rays for Occlusion-aware Image-based Rendering (2107.13421v2)

Abstract: We present a new neural representation, called Neural Ray (NeuRay), for the novel view synthesis task. Recent works construct radiance fields from image features of input views to render novel view images, which enables the generalization to new scenes. However, due to occlusions, a 3D point may be invisible to some input views. On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction. To solve this problem, we predict the visibility of 3D points to input views within our NeuRay representation. This visibility enables the radiance field construction to focus on visible image features, which significantly improves its rendering quality. Meanwhile, a novel consistency loss is proposed to refine the visibility in NeuRay when finetuning on a specific scene. Experiments demonstrate that our approach achieves state-of-the-art performance on the novel view synthesis task when generalizing to unseen scenes and outperforms per-scene optimization methods after finetuning.

• The paper introduces Neural Rays, a framework that enhances image synthesis by accurately predicting point visibility in occluded regions.
• It leverages a novel CDF-based parameterization to efficiently model occlusion effects, reducing computational overhead compared to density-based approaches.
• Experimental results on multiple datasets demonstrate superior generalization and fine-tuning performance in complex scenes with significant self-occlusions.

Neural Rays for Occlusion-aware Image-based Rendering

In recent advancements within the domain of neural rendering, the task of Novel View Synthesis (NVS) plays a crucial role, where the objective is to generate images from arbitrary viewpoints given a set of multi-view images with known camera parameters. The paper "Neural Rays for Occlusion-aware Image-based Rendering" introduces an innovative approach labeled as Neural Ray (NeuRay), designed explicitly to enhance the rendering quality by tackling occlusion challenges prevalent in current methodologies.

Classical approaches to NVS have leveraged Neural Radiance Fields (NeRF), focusing on scene-specific networks for image synthesis through volume rendering across a 5D radiance field. While these models deliver impressive photo-realistic outputs, their adaptiveness to new unseen scenes without extensive retraining remains a significant limitation. NeuRay addresses this by focusing on occlusion-aware rendering, a gap in existing radiance field construction methods that indiscriminately consider input views for rendering, often incorporating inconsistent features from occluded or invisible 3D points, thereby diminishing output quality.

The NeuRay framework extends the capabilities of NeRF-like rendering systems by explicitly predicting the visibility of each 3D point relative to input views. This improvement enhances both the precision and visual fidelity of synthesized images, primarily in circumstances with substantial self-occlusions. The novel visibility prediction is played out using a pixel-aligned neural representation that differentiates between occlusion-induced feature inconsistency and inconsistency stemming from non-surface points. This classification enables more accurate scene geometry recovery and color prediction through identifying relevant visible features.

A notable technical highlight of this method is the proposed Cumulative Distribution Function (CDF) based parameterization for visibility prediction, demonstrated as computationally efficient compared to the density-based formulations typical of other models. The CDF method allows visibility estimation with significantly reduced computational overhead, which is crucial when operating with numerous input views and dense sample points. This efficient visibility prediction not only underpins the superior real-time rendering application potential of NeuRay but also distinguishes it from existing approaches.

Furthermore, NeuRay introduces a fine-tuning process that allows adaptive refinement through scene-specific memorization of geometry, leading to significant post-training performance gains. The formulation of a novel consistency loss aligns the NeuRay representation with the constructed radiance field's rendered outputs, thus preserving accurate geometry depiction and enhancing occlusion inference reliability during rendering.

Experimental validation across multiple datasets, including the NeRF synthetic, DTU, and LLFF datasets, highlights NeuRay’s superior performance in both generalization and fine-tuning scenarios. It outperforms existing generalization methods like IBRNet, especially in rendering tasks involving complex scenes with significant occlusion. The results emphasize the efficacy of its occlusion-aware strategy, where NeuRay demonstrates its ability to outperform not only during generalization but also in scenarios requiring rapid fine-tuning.

The implications of introducing such a versatile, occlusion-aware rendering approach are multifaceted. On a practical front, it paves the way for more efficient and high-fidelity rendering applications in various domains including virtual reality, gaming, and visual effects. Theoretically, this work lays foundational insights for future developments in neural representations and image synthesis, potentially guiding novel frameworks integrating visibility prediction more centrally.

Looking ahead, further research may explore refining NeuRay's feature aggregation and visibility prediction components, exploring adaptive sampling techniques to enhance computational efficiency, or expanding its applicability across diverse and challenging scene compositions. Indeed, NeuRay represents a promising step forward in the continual evolution of neural rendering technologies.