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The key insight is that indirect illumination can be conveniently derived from the neural radiance field learned from input images instead of being estimated jointly with direct illumination and materials. In this paper, we propose a novel approach to efficiently recovering spatially-varying indirect illumination. Despite the promising results achieved, indirect illumination is rarely modeled in previous methods, as it requires expensive recursive path tracing which makes the inverse rendering computationally intractable. Recent advances in implicit neural representations and differentiable rendering make it possible to simultaneously recover the geometry and materials of an object from multi-view RGB images captured under unknown static illumination. To the best of our knowledge, this is the first work that extracts albedo-textured meshes with unposed 2D images without any additional labels or assumptions. In addition, we show that our approach improves the quality of the generated geometry via visualization and quantitative comparison.

We demonstrate the superiority of our method by visualizing factorized representations, re-lighted images, and albedo-textured meshes. To broaden the 3D applicability from 3D-aware image generation to 3D-controllable object generation, we propose the factorized representations which are view-independent and light-disentangled, and training schemes with randomly sampled light conditions. Due to their view-dependent and light-entangled volume representation, the 3D geometry presents unrealistic quality and the color should be re-rendered for every desired viewpoint. While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications. For more details and SIMBAR relit datasets, please visit our project website (). To further validate and effectively quantify the benefit of leveraging SIMBAR for data augmentation for automated driving vision tasks, object detection and tracking experiments are conducted with a state-of-the-art method, a Multiple Object Tracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack on SIMBAR-augmented KITTI - an impressive 9.0% relative improvement over the baseline MOTA of 85.6% with CenterTrack on original KITTI, both models trained from scratch and tested on Virtual KITTI. We present qualitative comparisons with prior multi-view scene relighting baselines.

To the best of our knowledge, there is no prior work on scene relighting leveraging explicit geometric representations from a single image. This paper presents a novel image-based relighting pipeline, SIMBAR, that can work with a single image as input.

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PROJECT D 2.31 RFACTOR DOWNLOAD HOW TO

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