This paper addresses the task of estimating the light arriving from all directions to a 3D point observed at a selected pixel in an RGB image. This task is challenging because it requires predicting a mapping from a partial scene observation by a camera to a complete illumination map for a selected position, which depends on the 3D location of the selection, the distribution of unobserved light sources, the occlusions caused by scene geometry, etc. Previous methods attempt to learn this complex mapping directly using a single black-box neural network, which often fails to estimate high-frequency lighting details for scenes with complicated 3D geometry. Instead, we propose "Neural Illumination" a new approach that decomposes illumination prediction into several simpler differentiable sub-tasks: 1) geometry estimation, 2) scene completion, and 3) LDR-to-HDR estimation. The advantage of this approach is that the sub-tasks are relatively easy to learn and can be trained with direct supervision, while the whole pipeline is fully differentiable and can be fine-tuned with end-to-end supervision. Experiments show that our approach performs significantly better quantitatively and qualitatively than prior work.
Optimization-based Methods One standard approach to estimating illumination is to jointly optimize the geometry, reflectance properties, and lighting models of the scene in order to find the set of values that best explain the observed input image. However, directly optimizing all scene parameters is often a highly under-constrained problem -- an error in one parameter estimation can easily propagate into another. Therefore to ease the optimization process, many prior methods either assume additional user-provided ground truth information as input or make strong assumptions about the lighting models. For example, Karsch al @cite_14 uses user annotations for initial lighting and geometry estimates. Zhang al @cite_16 uses manually-annotated light-source locations and assumes knowledge of depth information. Lombardi and Nishino @cite_4 propose approximating illumination with a low-dimensional model, which subsequently has been shown to be sub-optimal for indoor scenes due to object reflective and geometric properties @cite_23 .
@cite_23 @cite_14 @cite_4 @cite_16
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Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e.g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor outdoor) and this label data is made available on-line. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues. This paper describes a new method for estimating the illumination distribution of a real scene from a radiance distribution inside shadows cast by an object in the scene. First, the illumination distribution of the scene is approximated by discrete sampling of an extended light source. Then the illumination distribution of the scene is estimated from a radiance distribution inside shadows cast by an object of known shape onto another object in the scene. Instead of assuming any particular reflectance properties of the surface inside the shadows, both the illumination distribution of the scene and the reflectance properties of the surface are estimated simultaneously, based on iterative optimization framework. In addition, this paper introduces an adaptive sampling of the illumination distribution of a scene. Rather than using a uniform discretization of the overall illumination distribution, we adaptively increase sampling directions of the illumination distribution based on the estimation at the previous iteration. Using the adaptive sampling framework, we are able to estimate overall illumination more efficiently by using fewer sampling directions. The proposed method is effective for estimating an illumination distribution even under a complex illumination environment. We introduce a method to jointly estimate the BRDF and geometry of an object from a single image under known, but uncontrolled, natural illumination. We show that this previously unexplored problem becomes tractable when one exploits the orientation clues embedded in the lighting environment. Intuitively, unique regions in the lighting environment act analogously to the point light sources of traditional photometric stereo; they strongly constrain the orientation of the surface patches that reflect them. The reflectance, which acts as a bandpass filter on the lighting environment, determines the necessary scale of such regions. Accurate reflectance estimation, however, relies on accurate surface orientation information. Thus, these two factors must be estimated jointly. To do so, we derive a probabilistic formulation and introduce priors to address situations where the reflectance and lighting environment do not sufficiently constrain the geometry of the object. Through extensive experimentation we show what this space looks like, and offer insights into what problems become solvable in various categories of real-world natural illumination environments. We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, produces photo-realistic results that we validate via a perceptual user study.