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Related papers: IRIS: Inverse Rendering of Indoor Scenes from Low …

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A concept of light-fields computed from multiple view images on regular grids has proven its benefit for scene representations, and supported realistic renderings of novel views and photographic effects such as refocusing and shallow depth…

Computer Vision and Pattern Recognition · Computer Science 2025-10-24 Hyunjun Jung , Hae-Gon Jeon

High dynamic range imaging (HDRI) for real-world dynamic scenes is challenging because moving objects may lead to hybrid degradation of low dynamic range and motion blur. Existing event-based approaches only focus on a separate task, while…

Computer Vision and Pattern Recognition · Computer Science 2024-04-05 Li Xiaopeng , Zeng Zhaoyuan , Fan Cien , Zhao Chen , Deng Lei , Yu Lei

This paper proposes a practical photometric solution for the challenging problem of in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a…

Computer Vision and Pattern Recognition · Computer Science 2023-03-27 Ziang Cheng , Junxuan Li , Hongdong Li

In this work, we address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as image-to-image translation, ignoring the 3D properties of…

Computer Vision and Pattern Recognition · Computer Science 2021-10-22 Zian Wang , Jonah Philion , Sanja Fidler , Jan Kautz

Editing High Dynamic Range (HDR) environment maps using an inverse differentiable rendering architecture is a complex inverse problem due to the sparsity of relevant pixels and the challenges in balancing light sources and background. The…

Computer Vision and Pattern Recognition · Computer Science 2024-10-25 Antonio D'Orazio , Davide Sforza , Fabio Pellacini , Iacopo Masi

We present a learning-based technique for estimating high dynamic range (HDR), omnidirectional illumination from a single low dynamic range (LDR) portrait image captured under arbitrary indoor or outdoor lighting conditions. We train our…

Computer Vision and Pattern Recognition · Computer Science 2020-08-07 Chloe LeGendre , Wan-Chun Ma , Rohit Pandey , Sean Fanello , Christoph Rhemann , Jason Dourgarian , Jay Busch , Paul Debevec

Reconstructing objects from posed images is a crucial and complex task in computer graphics and computer vision. While NeRF-based neural reconstruction methods have exhibited impressive reconstruction ability, they tend to be…

Computer Vision and Pattern Recognition · Computer Science 2024-10-18 Shuichang Lai , Letian Huang , Jie Guo , Kai Cheng , Bowen Pan , Xiaoxiao Long , Jiangjing Lyu , Chengfei Lv , Yanwen Guo

We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we create a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying,…

Computer Vision and Pattern Recognition · Computer Science 2019-05-09 Zhengqin Li , Mohammad Shafiei , Ravi Ramamoorthi , Kalyan Sunkavalli , Manmohan Chandraker

Inverse rendering aims to reconstruct geometry and reflectance from captured images. Display-camera imaging systems offer unique advantages for this task: each pixel can easily function as a programmable point light source, and the…

Graphics · Computer Science 2025-08-21 Seokjun Choi , Hoon-Gyu Chung , Yujin Jeon , Giljoo Nam , Seung-Hwan Baek

Multi-view inverse rendering aims to recover geometry, materials, and illumination consistently across multiple viewpoints. When applied to multi-view images, existing single-view approaches often ignore cross-view relationships, leading to…

Computer Vision and Pattern Recognition · Computer Science 2025-12-30 Xiangzuo Wu , Chengwei Ren , Jun Zhou , Xiu Li , Yuan Liu

We present Large Inverse Rendering Model (LIRM), a transformer architecture that jointly reconstructs high-quality shape, materials, and radiance fields with view-dependent effects in less than a second. Our model builds upon the recent…

Computer Vision and Pattern Recognition · Computer Science 2025-04-29 Zhengqin Li , Dilin Wang , Ka Chen , Zhaoyang Lv , Thu Nguyen-Phuoc , Milim Lee , Jia-Bin Huang , Lei Xiao , Cheng Zhang , Yufeng Zhu , Carl S. Marshall , Yufeng Ren , Richard Newcombe , Zhao Dong

Accurate material retrieval is critical for creating realistic 3D assets. Existing methods rely on datasets that capture shape-invariant and lighting-varied representations of materials, which are scarce and face challenges due to limited…

Computer Vision and Pattern Recognition · Computer Science 2025-04-09 Jianhui Wang , Zhifei Yang , Yangfan He , Huixiong Zhang , Yuxuan Chen , Jingwei Huang

We present UrbanIR (Urban Scene Inverse Rendering), a new inverse graphics model that enables realistic, free-viewpoint renderings of scenes under various lighting conditions with a single video. It accurately infers shape, albedo,…

Computer Vision and Pattern Recognition · Computer Science 2025-01-16 Chih-Hao Lin , Bohan Liu , Yi-Ting Chen , Kuan-Sheng Chen , David Forsyth , Jia-Bin Huang , Anand Bhattad , Shenlong Wang

In this work, we propose an inverse rendering model that estimates 3D shape, spatially-varying reflectance, homogeneous subsurface scattering parameters, and an environment illumination jointly from only a pair of captured images of a…

Computer Vision and Pattern Recognition · Computer Science 2023-05-16 Chenhao Li , Trung Thanh Ngo , Hajime Nagahara

High Dynamic Range (HDR) imaging aims to reproduce the wide range of brightness levels present in natural scenes, which the human visual system can perceive but conventional digital cameras often fail to capture due to their limited dynamic…

Image and Video Processing · Electrical Eng. & Systems 2025-10-28 Kumbha Nagaswetha

Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content. The dynamic range of well-exposed areas must be expanded and any missing information due to…

Computer Vision and Pattern Recognition · Computer Science 2021-06-18 Demetris Marnerides , Thomas Bashford-Rogers , Kurt Debattista

A single rolling-shutter (RS) image may be viewed as a row-wise combination of a sequence of global-shutter (GS) images captured by a (virtual) moving GS camera within the exposure duration. Although RS cameras are widely used, the RS…

Computer Vision and Pattern Recognition · Computer Science 2022-10-07 Bin Fan , Yuchao Dai , Hongdong Li

High dynamic range (HDR) imaging aims to obtain a high-quality HDR image by fusing information from multiple low dynamic range (LDR) images. Numerous learning-based HDR imaging methods have been proposed to achieve this for static and…

Image and Video Processing · Electrical Eng. & Systems 2025-05-30 Yu Yuan , Yiheng Chi , Xingguang Zhang , Stanley Chan

The prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured images turn out to…

Computer Vision and Pattern Recognition · Computer Science 2021-10-18 Prarabdh Raipurkar , Rohil Pal , Shanmuganathan Raman

In inverse rendering, accurately modeling visibility and indirect radiance for incident light is essential for capturing secondary effects. Due to the absence of a powerful Gaussian ray tracer, previous 3DGS-based methods have either…

Computer Vision and Pattern Recognition · Computer Science 2025-03-25 Chun Gu , Xiaofei Wei , Zixuan Zeng , Yuxuan Yao , Li Zhang