NeROIC: Neural Rendering of Objects from Online Image Collections (2201.02533v2)

Abstract: We present a novel method to acquire object representations from online image collections, capturing high-quality geometry and material properties of arbitrary objects from photographs with varying cameras, illumination, and backgrounds. This enables various object-centric rendering applications such as novel-view synthesis, relighting, and harmonized background composition from challenging in-the-wild input. Using a multi-stage approach extending neural radiance fields, we first infer the surface geometry and refine the coarsely estimated initial camera parameters, while leveraging coarse foreground object masks to improve the training efficiency and geometry quality. We also introduce a robust normal estimation technique which eliminates the effect of geometric noise while retaining crucial details. Lastly, we extract surface material properties and ambient illumination, represented in spherical harmonics with extensions that handle transient elements, e.g. sharp shadows. The union of these components results in a highly modular and efficient object acquisition framework. Extensive evaluations and comparisons demonstrate the advantages of our approach in capturing high-quality geometry and appearance properties useful for rendering applications.

• The paper presents a multi-stage neural rendering method that decouples geometry inference from material and lighting extraction, enhancing object reconstruction.
• It utilizes a Bayesian framework and grid-based normal extraction to robustly address occlusions and varying lighting in uncontrolled image collections.
• Extensive experiments show improved PSNR and LPIPS scores over standard NeRF, highlighting its potential for augmented reality and digital asset creation.

An Analysis of "NeROIC: Neural Rendering of Objects from Online Image Collections"

The paper "NeROIC: Neural Rendering of Objects from Online Image Collections" presents an innovative approach for extracting high-fidelity geometry and material properties of objects using image collections available online. The authors build upon the foundational concept of Neural Radiance Fields (NeRF) to address the challenges associated with rendering objects captured under varying uncontrolled conditions—such as diverse lighting and different camera settings.

Methodological Advancements

Central to NeROIC's methodology is the multi-stage process that isolates the geometry and improves camera parameter estimation. This is achieved while utilizing foreground object masks to bolster training efficiency and output quality. The approach strategically separates the geometry inference phase from material and lighting parameter extraction, allowing for modular training and increased optimization efficiency.

In the geometry stage, NeROIC employs a Bayesian learning framework with transient and static components to capture and correct discrepancies in lighting and occlusion, distinguishing it from traditional NeRF models which do not factor in transient variations. By doing this, the robustness of object geometry capture is significantly enhanced, even when dealing with imperfect initial camera parameters. Furthermore, a novel silhouette loss and adaptive sampling strategy are introduced to refine the object's geometry representation concerning the varied, real-world backgrounds typically found in online image collections.

For estimating normals, the paper introduces a unique grid-based normal extraction layer. This layer significantly mitigates the influences of noise in the density function, which is typical when dealing with broadly varying and uncalibrated image inputs, thus ensuring more consistent normal estimation and finer material property extraction.

The second stage of NeROIC's pipeline focuses on rendering network training, where it refines the lighting and material properties of objects. By representing lighting conditions in spherical harmonics, NeROIC is capable of performing novel view synthesis and allows for relighting under different environmental conditions. This capability to relight extends its practical applications in augmented reality and scene composition technologies.

Evaluation and Implications

The proposed method is validated through extensive experiments. NeROIC demonstrates superior performance over baselines like NeRF and more specialized decomposition methods such as NeRD and NeRFactor, particularly in datasets characterized by complex and uncontrolled image conditions. Quantitatively, NeROIC achieves better PSNR and LPIPS scores compared to standard NeRF implementations, an indication of improved fidelity in geometry capturing and rendering.

Practically, the ramifications of NeROIC are profound in any domain requiring high-fidelity 3D object reconstructions from non-specialized image data. Industries relying on asset digitization, virtual reality, and e-commerce could particularly benefit, given the capability of capturing and rendering objects with minimal imagery and no need for sophisticated image acquisition or uniformity in image conditions.

Future Directions

The NeROIC framework could be enriched by exploring integration with more advanced camera pose estimation algorithms or by incorporating adaptive neural network architectures to handle even more complex reflections and shadow effects not perfectly captured by the current spherical harmonic lighting representation. Furthermore, expanding its applicability to a wider range of object types, from highly reflective surfaces to transparent materials, could see increased adoption in sectors like automotive, manufacturing, and virtual try-ons for digital fashion and accessories.

Overall, "NeROIC: Neural Rendering of Objects from Online Image Collections" offers a significant advance in the field of neural rendering, pushing the boundaries of how object representations can be intuitively and efficiently extracted from widely varying and non-calibrated image datasets.