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

TL;DR

ARC-NeRF tackles the challenge of few-shot novel-view synthesis by introducing Area Ray Casting, which featurizes a bundle of rays via Integrated Positional Encoding to cover a broader region of unseen views with a single augmented sample. It further introduces adaptive high-frequency regularization driven by target-pixel photo-consistency and a luminance-consistency regularization using a luminance map derived from RGB images, enabling sharper textures without manual masking schedules. Empirically, ARC-NeRF achieves state-of-the-art or competitive results on Realistic Synthetic 360°, DTU, and Shiny Blender datasets, outperforming baselines like FlipNeRF, RegNeRF, and FreeNeRF, and shows clear ablations supporting the effectiveness of Area Rays and luminance regularization. The approach reduces reliance on heavy pre-training and demonstrates practical gains for few-shot rendering, with potential for extension to more complex or unbounded scenes in the future.

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.

Figures (10)

• Figure 1: Comparison with other baselines. Our ARC-NeRF achieves superior quality of fine details and textures to other state-of-the-art methods by using our Area Rays equipped with an adaptive high-frequency regularization. The last two rows of the figure indicate the types of ray augmentation schemes and frequency regularization methods, respectively.
• Figure 2: Comparative overview of ray augmentation techniques. In contrast to other augmentation strategies ((b), (c)), where each additional ray corresponds to a single unseen view, our Area Ray (d) encompasses a wider area of unseen views, thereby boosting the augmentation's efficacy. Technically, the rays are spanned over the object surface as used in the typical NeRF's volume rendering technique, but we simplify the illustration for easy comparison between the methods. Kindly refer to \ref{['fig:hg_generation']} for a more detailed description. The blue and red rays indicate the original and augmented ray, respectively.
• Figure 3: Area Ray generation process. (a) First, to derive $\tilde{\sigma}^2_\rho$, we reparameterize the original metric distance $t$ as $\tilde{t}$. We shift $t_1$, i.e. starting point of a ray along a z-axis, to the estimated object surface $\mathbf{p}_s$, so that our proposed Area Ray is symmetrically constructed around $\mathbf{p}_s$, i.e.$\tilde{t}_{s-i} = \tilde{t}_{s+i}$ and it leads to $\boldsymbol{\tilde{\Sigma}}_{s-i} = \boldsymbol{\tilde{\Sigma}}_{s+i}$. Note that $\tilde{t}$ is used only for $\tilde{\sigma}^2_\rho$. (b) Using the trigonometric function, we compute the base radius of Area Ray $\tilde{\rho}$. As a result, our proposed Area Ray $\mathbf{\tilde{r}}$ is featurized to cover the unseen view area between the original ray and its reflection ray. The blue dotted ray denotes the reflection ray of $\mathbf{r}$.
• Figure 4: Comparison of fine details against FreeNeRF over the training phase. Compared to FreeNeRF which forcibly masks the high-frequency spectrum in the early training phase, ours adaptively regularizes the high-frequency components of additional ray samples based on the target pixel photo-consistency (i.e. the angle between the original ray and Area Ray) during the whole training process. As a result, our ARC-NeRF already achieves sharper fine details at 25K iteration than the fully trained FreeNeRF.
• Figure 5: Comparison of our ARC-NeRF against multicasting strategy on DTU 3-view. Our ARC-NeRF outperforms FlipNeRF in all scenarios by a large margin. The training time per scene is measured using the same GPU, iterations, and batch size. The size of circles is proportional to $\kappa$, i.e. the number of augmented rays per original ray.