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Differentiable Rendering: a Survey JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Differentiable Rendering: A Survey Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl and Adrien Gaidon Abstract— Deep neural networks (DNNs) have shown remarkable performance improvements on vision-related tasks such as object detection or image segmentation. Despite their success, they generally lack the understanding of 3D objects which form the image, as it is not always possible to collect 3D information about the scene or to easily annotate it. Differentiable rendering is a novel field which allows the gradients of 3D objects to be calculated and propagated through images. It also reduces the requirement of 3D data collection and annotation, while enabling higher success rate in various applications. This paper reviews existing literature and discusses the current state of differentiable rendering, its applications and open research problems. Index Terms—Differentiable Rendering, Inverse Graphics, Analysis-by-Synthesis F 1 INTRODUCTION Despite its potential, using existing or developing new DR methods is not straightforward. This can be attributed The last years have clearly shown that neural networks to four reasons: are effective for 2D and 3D reasoning [1], [2], [3], [4], [5], [6]. However, most 3D estimation methods rely on • Many DR-based methods have been published over supervised training regimes and costly annotations, which the past few years. To understand which ones are makes the collection of all properties of 3D observations suitable for addressing certain types of problems, challenging. Hence, there have been recent efforts towards a thorough understanding of the methods’ mech- leveraging easier-to-obtain 2D information and differing anisms as well as the underlying properties is re- levels of supervision for 3D scene understanding. One of quired. the approaches is integrating graphical rendering processes • In order to choose or develop a novel DR method, into neural network pipelines. This allows transforming and the evaluation methodology of the existing methods incorporating 3D estimates into 2D image level evidence. should be known. Rendering in computer graphics is the process of gener- • In order to use DR in a novel task, it is necessary ating images of 3D scenes defined by geometry, materials, to survey the usage of DR in existing applications. scene lights and camera properties. Rendering is a complex However, due to the variety of applications, it is not process and its differentiation is not uniquely defined, which trivial to have a clear view of the field. prevents straightforward integration into neural networks. • Several DR libraries have emerged over the past Differentiable rendering (DR) constitutes a family of years with each focusing on different aspects of the techniques that tackle such an integration for end-to-end differentiable rendering process. This makes some optimization by obtaining useful gradients of the rendering of the libraries useful for a particular type of ap- process. By differentiating the rendering, DR bridges the gap plications, while for others, additional functionality between 2D and 3D processing methods, allowing neural may need to be implemented from scratch. Also, cer- networks to optimize 3D entities while operating on 2D tain applications are constrained by computational projections. As shown in Figure 1, optimization of the 3D arXiv:2006.12057v2 [cs.CV] 31 Jul 2020 requirements that existing implementations of DR- scene parameters can be achieved by backpropagating the based methods often do not fulfill. This is especially gradients with respect to the rendering output. The com- the case for real-time applications or embedded de- mon 3D self-supervision pipeline is applied by integrating vices, for which highly optimized neural networks the rendering layer to the predicted scene parameters and are necessary. applying the loss by comparing the rendered and input image in various ways. The applications of this process are To address these shortcomings, the current state of DR broad, including image-based training of 3D object recon- needs to be properly surveyed. However, to the best of our struction [7], [8], [9], human pose estimation [10], [11], hand knowledge, there has been no comprehensive review for this pose estimation [12], [13] and face reconstruction [14], [15]. purpose to date. In this work, we provide an overview of the current • Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando and Toru state of DR algorithms in Section 2, the evaluation metrics in Matsuoka are with Preferred Networks, Inc. E-mail: fhkato, dbeker, Section 3, the applications that use differentiable rendering mmorariu, ando, [email protected] • Wadim Kehl is with Toyota Research Institute - Advanced Development. in Section 4 and the libraries that are currently being used to E-mail: [email protected] facilitate the research in Section 5. Besides offering a survey • Adrien Gaidon is with Toyota Research Institute. E-mail: on the existing methods, we also discuss open research [email protected] problems and provide suggestions for future work. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 2 Meta Materials Image RGB Lights Image Objective Ground Render Function Truth Depth Cameras Image Silhouette Geometry Image Scene Parameters Rendering Output Optimization using a Differentiable Renderer Scene RGB Image Parameters Image Render Neural Network Learnable parameters Output data Input data Common Self-Supervision Pipeline with Differentiable Rendering Functions Fig. 1. Schematic overview of differentiable rendering. Best viewed in color. The top part shows a basic optimization pipeline using a differentiable renderer where the gradients of an objective function with respect to the scene parameters and known ground-truth are calculated. The bottom part shows a common self-supervision pipeline based on differentiable rendering. Here, the supervision signal is provided in the form of image evidence and the neural network is updated by backpropagating the error between the image and the rendering output. 2 ALGORITHMS to the input parameters @I=@Φ in order to optimize for a specific goal (loss function). The computation of these gra- TABLE 1 dients can be approximate, but should be accurate enough Overview of the representative differentiable rendering methods. They to propagate meaningful information required to minimize are classified by the four main underlying data representations. the objective function. The inputs to a rendering function Φ, especially the Data Repr Type Literature geometric parameters Φs, are some of the main differentia- tors between the differentiable rendering algorithms. Each Analytical derivative [16], [17], [18] Approx. gradient [19], [9], [20], [14] data representation has its own strengths which makes it Mesh Approx. rendering [21], [22], [23] suitable for solving specific problems. In this work we group Global illumination [24], [25], [26], [27], the surveyed DR algorithms into four categories, based [28] on the underlying data representation: mesh, voxels, point Occupancy [7], [29], [30], [31] clouds and neural implicit representations. We also discuss Voxel SDF [32] neural rendering, as there is a growing body of research into learning to render using neural network models, rather than Point cloud [33], [34], [35], [36], Point cloud [37], [38] designing the rendering and its differentiation manually. RGBD image [39], [40] Table 1 lists the literature that we are covering in this section. Occupancy [41], [42] Implicit Level set [43], [44], [45], [46] 2.1 Mesh 2.1.1 Analytical Derivative of Rendering We start by briefly defining a mathematical formulation A mesh represents a 3D shape as a set of vertices and the for our purposes. A rendering function R takes shape surfaces that connect them. It is widely used, especially in parameters Φs, camera parameters Φc, material parameters computer graphics, because it can represent complex 3D Φm and lighting parameters Φl as input and outputs an shapes in a compact way. RGB image Ic or a depth image Id. We denote the inputs Given the rendering inputs and a pixel, the process of as Φ = fΦs; Φm; Φc; Φlg and the outputs as I = fIc;Idg. determining its color can be divided into (1) assigning a Note that a general DR formulation can have different kinds triangle to the pixel and (2) computing the color of the of additional input/output entities, but in this section we pixel based on the colors of the assigned triangle’s vertices. refer to the most common ones. A differentiable renderer The former is done by projecting all mesh triangles from computes the gradients of the output image with respect world space to screen space and determining the ones that JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 3 (a) Weight of pixel depth and color with respect to the input parameters Pixel coordinates (e) Depth vertices can be computed analytically [16], [17], [18]. In standard rendering, exactly one triangle per pixel is (b) Triangle in Triangle screen space typically selected to compute the final color value, which can lead to optimization problems. To exemplify, let us (c) Triangle in Camera consider a scene composed of one white triangle and two camera space black triangles as illustrated in Figure 3. The vertex color of w b (d) Light in vi is 1 and the vertex color of vi is 0. Then, using barycentric Light P w P camera space coordinates wi that satisfy vp = wivi and wi = 1, the P v c = w c w = 1 color of a pixel at p is a constant vp i vi . Material (f) RGB w b Therefore, the gradient of cvp with respect to vi and vi is zero. Fig. 2. Several operations that are performed inside a rendering function, Similarly, the derivative of a pixel color with respect given a pixel, its corresponding triangle and material defined on vertices to a vertex position is always zero for all pixels and ver- of the triangle, camera parameters, and light configurations. The green tices.
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