Sparse Needlets for Lighting Estimation with Spherical Transport Loss

• URL: http://arxiv.org/abs/2106.13090v1
• Date: Thu, 24 Jun 2021 15:19:42 GMT
• Title: Sparse Needlets for Lighting Estimation with Spherical Transport Loss
• Authors: Fangneng Zhan, Changgong Zhang, Wenbo Hu, Shijian Lu, Feiying Ma, Xuansong Xie, Ling Shao
• Abstract summary: NeedleLight is a new lighting estimation model that represents illumination with needlets and allows lighting estimation in both frequency domain and spatial domain jointly. Extensive experiments show that NeedleLight achieves superior lighting estimation consistently across multiple evaluation metrics as compared with state-of-the-art methods.
• Score: 89.52531416604774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate lighting estimation is challenging yet critical to many computer vision and computer graphics tasks such as high-dynamic-range (HDR) relighting. Existing approaches model lighting in either frequency domain or spatial domain which is insufficient to represent the complex lighting conditions in scenes and tends to produce inaccurate estimation. This paper presents NeedleLight, a new lighting estimation model that represents illumination with needlets and allows lighting estimation in both frequency domain and spatial domain jointly. An optimal thresholding function is designed to achieve sparse needlets which trims redundant lighting parameters and demonstrates superior localization properties for illumination representation. In addition, a novel spherical transport loss is designed based on optimal transport theory which guides to regress lighting representation parameters with consideration of the spatial information. Furthermore, we propose a new metric that is concise yet effective by directly evaluating the estimated illumination maps rather than rendered images. Extensive experiments show that NeedleLight achieves superior lighting estimation consistently across multiple evaluation metrics as compared with state-of-the-art methods.

Related papers

• MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models [69.39388799906409]
  Existing works estimate illumination by generating illumination maps or regressing illumination parameters. This paper presents MixLight, a joint model that utilizes the complementary characteristics of SH and SG to achieve a more complete illumination representation.
  arXiv  Detail & Related papers  (2024-04-19T10:17:10Z)
• LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation [4.079873017864992]
  We present a lightweight solution for estimating spatially-coherent indoor lighting from a single RGB image. We introduce a unified, voxel octree-based illumination estimation framework to produce 3D spatially-coherent lighting.
  arXiv  Detail & Related papers  (2024-04-05T07:15:06Z)
• Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network [69.96295927854042]
  Low-light environments usually lead to less informative large-scale dark areas. We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks. Because exponential operation introduces high computational complexity, we propose to use Taylor Series to approximate gamma correction.
  arXiv  Detail & Related papers  (2023-08-16T08:46:51Z)
• TensoIR: Tensorial Inverse Rendering [51.57268311847087]
  TensoIR is a novel inverse rendering approach based on tensor factorization and neural fields. TensoRF is a state-of-the-art approach for radiance field modeling.
  arXiv  Detail & Related papers  (2023-04-24T21:39:13Z)
• Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion [129.52943959497665]
  Existing works on outdoor lighting estimation typically simplify the scene lighting into an environment map. We propose a neural approach that estimates the 5D HDR light field from a single image. We show the benefits of our AR object insertion in an autonomous driving application.
  arXiv  Detail & Related papers  (2022-08-19T17:59:16Z)
• GMLight: Lighting Estimation via Geometric Distribution Approximation [86.95367898017358]
  This paper presents a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation. We parameterize illumination scenes in terms of the geometric light distribution, light intensity, ambient term, and auxiliary depth, and estimate them as a pure regression task. With the estimated lighting parameters, the generative projector synthesizes panoramic illumination maps with realistic appearance and frequency.
  arXiv  Detail & Related papers  (2021-02-20T03:31:52Z)
• EMLight: Lighting Estimation via Spherical Distribution Approximation [33.26530733479459]
  We propose an illumination estimation framework that leverages a regression network and a neural projector for accurate illumination estimation. We decompose the illumination map into spherical light distribution, light intensity and the ambient term. Under the guidance of the predicted spherical distribution, light intensity and ambient term, the neural projector synthesizes panoramic illumination maps with realistic light frequency.
  arXiv  Detail & Related papers  (2020-12-21T04:54:08Z)
• Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement [19.955016389978926]
  Low-light image enhancement is a promising solution to tackle the problem of insufficient sensitivity of human vision system (HVS) to perceive information in low light environments. Previous Retinex-based works always accomplish enhancement task by estimating light intensity. We propose a visual perception (VP) model to acquire a precise mathematical description of visual perception.
  arXiv  Detail & Related papers  (2020-05-15T03:47:10Z)
• PointAR: Efficient Lighting Estimation for Mobile Augmented Reality [7.58114840374767]
  We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices. PointAR takes a single RGB-D image captured from the mobile camera and a 2D location in that image, and estimates 2nd order spherical harmonics coefficients.
  arXiv  Detail & Related papers  (2020-03-30T19:13:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.