MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.

ExtremeNeRF: Few-shot Neural Radiance Fields Under Unconstrained Illumination

In this paper, we propose a new challenge that synthesizes a novel view in a more practical environment, where the number of input multi-view images is limited and illumination variations are significant. Despite recent success, neural radiance fields (NeRF) require a massive amount of input multi-view images taken under constrained illuminations. To address the problem, we suggest ExtremeNeRF, which utilizes occlusion-aware multiview albedo consistency, supported by geometric alignment and depth consistency. We extract intrinsic image components that should be illumination-invariant across different views, enabling direct appearance comparison between the input and novel view under unconstrained illumination. We provide extensive experimental results for an evaluation of the task, using the newly built NeRF Extreme benchmark, which is the first in-the-wild novel view synthesis benchmark taken under multiple viewing directions and varying illuminations. The project page is at https://seokyeong94.github.io/ExtremeNeRF/