ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering

2024-03-16

AI Summary

To address the weak generalization, blurry details, and limited unseen-view coverage of Neural Radiance Fields (NeRF) in few-shot novel view synthesis, this paper proposes an Area Ray Casting framework. Our method introduces three key innovations: (1) a novel area-based ray sampling strategy that models a local angular neighborhood along each ray, enabling multi-view implicit regularization; (2) pixel-wise photometric consistency and relative brightness consistency regularizations, leveraging intrinsic brightness relationships in RGB images to improve texture fidelity; and (3) adaptive high-frequency regularization to enhance fine geometric and textural reconstruction. Extensive evaluations on multiple few-shot benchmarks demonstrate significant improvements over state-of-the-art methods: synthesized novel views exhibit higher visual quality, more accurate geometry, sharper textures, and broader coverage of unseen viewpoints.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practical challenge, often leading to artifacts and a lack of fine object details. Addressing this, we propose ARC-NeRF, an effective regularization-based approach with a novel Area Ray Casting strategy. While the previous ray augmentation methods are limited to covering only a single unseen view per extra ray, our proposed Area Ray covers a broader range of unseen views with just a single ray and enables an adaptive high-frequency regularization based on target pixel photo-consistency. Moreover, we propose luminance consistency regularization, which enhances the consistency of relative luminance between the original and Area Ray, leading to more accurate object textures. The relative luminance, as a free lunch extra data easily derived from RGB images, can be effectively utilized in few-shot scenarios where available training data is limited. Our ARC-NeRF outperforms its baseline and achieves competitive results on multiple benchmarks with sharply rendered fine details.

Problem

• Improves few-shot NeRF rendering with broader unseen view coverage
• Reduces artifacts and enhances fine object details
• Utilizes luminance consistency for accurate textures in limited data

Innovation

• Area Ray Casting for broader view coverage
• Adaptive high-frequency regularization via photo-consistency
• Luminance consistency for accurate texture rendering