ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering
This addresses practical challenges in 3D reconstruction and novel view synthesis for computer vision applications, representing an incremental improvement over existing regularization methods.
The paper tackles the problem of Neural Radiance Field (NeRF) artifacts and lack of fine details in few-shot object rendering by proposing ARC-NeRF with Area Ray Casting, achieving competitive results on multiple benchmarks with sharply rendered details.
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.