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In: Proceedings of International Conference on Computer Vision (ICCV), 2019
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Neural inverse rendering of an indoor scene from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. Fast spatially-varying indoor lighting estimation. Neural illumination: lighting prediction for indoor environments. New York: Association for Computing Machinery, 2019 In: Proceedings of ACM SIGGRAPH 2019 Talks. Deeplight: learning illumination for unconstrained mobile mixed reality. Deep sky modeling for single image outdoor lighting estimation. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2017
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Hold-Geoffroy Y, Sunkavalli K, Hadap S, et al. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, 2001. Image-based rendering of diffuse, specular and glossy surfaces from a single image. Inverse lighting and photorealistic rendering for augmented reality. Automatic scene inference for 3D object compositing. In: Proceedings of the 2nd IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Photorealistic rendering for augmented reality using environment illumination. 189–198Īgusanto K, Li L, Chuangui Z, et al. New York: Association for Computing Machinery, 1998. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques. Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. Dovenet: deep image harmonization via domain verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. In: Proceedings of 2007 IEEE 11th International Conference on Computer Vision, 2007. Using color compatibility for assessing image realism. CG2Real: improving the realism of computer generated images using a large collection of photographs. In: Proceedings of European Conference on Computer Vision.
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Sunkavalli K, Johnson M K, Matusik W, et al. In: Proceedings of the 4th European Conference on Visual Media Production, 2007. The linear monge-kantorovitch linear colour mapping for example-based colour transfer. Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. Working on a single photograph, our method can produce realistic reflections in a real scene with spatially-varying material and cast shadows on background objects with unknown geometry and material at real-time frame rates. We assume low-frequency lighting environments and adopt PRT (precomputed radiance transfer) for layer rendering, which makes the whole pipeline differentiable and enables fast end-to-end network training with synthetic scenes. The method starts from estimating the lighting and roughness information from the photograph using neural networks, renders the virtual object with a virtual floor into color, shadow and reflection layers by applying the estimated lighting, and finally refines the reflection and shadow layers using neural networks and blends them with the color layer and input image to yield the output image. We present neural compositing, a deep-learning based method for augmented reality rendering, which uses convolutional neural networks to composite rendered layers of a virtual object with a real photograph to emulate shadow and reflection effects.