Data Driven SVBRDF Estimation Using Deep Embedded Clustering

Abstract

Photo-realistic representation in user-specified view and lighting conditions is a challenging but high-demand technology in the digital transformation of cultural heritages. Despite recent advances in neural renderings, it is still necessary to capture high-quality surface reflectance from photography in a controlled environment for real-time applications such as VR/AR and digital arts. In this paper, we present a deep embedding clustering network for spatially-varying bidirectional reflectance distribution function (SVBRDF) estimation. Our network is designed to simultaneously update the reflectance basis and its linear manifold in the spatial domain of SVBRDF. We show that our dual update scheme excels in optimizing the rendering loss in terms of the convergence speed and visual quality compared to the current iterative SVBRDF update methods.

1. Introduction

Realistic representation of surface reflectance is a well-known but still challenging problem in computer graphics and vision. For an opaque object, it has been commonly modeled using a Bidirectional reflectance distribution function (BRDF) when the material has homogeneous reflectance properties. However, for an opaque object with inhomogeneous reflectance properties, which occurs when representing a material more realistically, the surface reflectance needs to be represented by a spatially-varying bidirectional reflectance distribution function (SVBRDF). A rich body of studies has estimated various types of SVBRDF using linear decomposition [1,2,3,4,5], independent BRDF reconstruction [6,7,8], and local linear embedding using BRDF examples [9,10,11,12].

The aforementioned methods require a dense sample of surface reflectance at multiple views under given lighting conditions since they are weak for generalization ability for which we can leverage inter-sample variability on SVBRDF estimation. For example, deep learning models further generalize model constraints using self-augmentation [13,14], random mixture of procedural SVBRDFs [15], or rendering with priors on shape and reflectance [16,17,18]. These methods are able to capture SVBRDF from a few images based on reflectance basis generated by prior information or self-augmentation scheme. Recently, neural radiance fields (NeRFs) have become a mainstream technology for reflectance estimation. They can learn a sparse representation of surface reflectance via view synthesis [19,20]. Beneficial to implicit view synthesis, NeRFs alleviate the complexity of the image capturing process.

However, there is still a high demand for full-sampling of surface reflectance, especially for a non-planar object in cultural heritages and AR/VR [21,22]. Due to the fact that cultural artifacts often consist of a non-planar geometry and a complex surface reflectance including non-diffuse reflectance properties, the current deep learning approaches have several limitations when applied to applications in which a high-quality reproduction of surface reflectance is required. Conceptually, their methods are capable of approximating SVBRDF with sparse samples. However, practically, their solution space is limited to a flat surface [13,14,15], or a single view [16,17,18]. In the same manner, recent advances in NeRFs allow us to handle global illumination changes. However, their solutions are yet bounded for a simple object with a parametric BRDF [23].

In this paper, we present a novel unsupervised deep learning-based method to estimate SVBRDFs from full reflectance samples. In contrast to the recent deep learning networks that infer a parametric BRDF [13,18,23,24], we adopted a general microfacet model [25] using an isotropic Normal Distribution Function (NDF). Our network trains for a non-parametric tabular form of basis BRDFs which allows a better representation of reflectance than the BRDFs using a parametric NDF such as Beckmann [25] and GGX [26]. Furthermore, our network architecture is designed to simultaneously learn a linear reflectance manifold spanned by NDF basis and the NDF basis, itself, by a novel autoencoder structure with two custom layers: the Reprojection layer that updates the NDF basis via rendering loss, and the Distance measure layer that remaps NDF basis in the spatial domain. These properties allow for a fast and more accurate approximation for SVBRDF compared to the current iterative update scheme [1,2,27,28] by the dual gradient search scheme.

The key observation found is that a gradient on a linear manifold of SVBRDF can be represented by a function of both basis BRDFs and the corresponding feature points in the manifold. Based on this observation, we have designed a non-linear autoencoder that simultaneously updates both the basis BRDFs and a spatial weight function that maps the observed reflectance to a linear manifold of the basis BRDFs. Our network design is inspired by the concept of the Deep Embedded Clustering (DEC) [29] which facilitates a joint-optimization of a mapping function and the clusters in an embedded space at the same time. In order to employ their strategy to learn a faithful linear manifold of the SVBRDF, we designed two custom layers that perform to measure similarity between reflectance at a surface point and the basis BRDFs and to render through basis BRDFs.

We believe that it yields a better solution in terms of accuracy and convergence speed compared to the existing methods which have fixed basis functions when forming a linear manifold [3,30], or updating the basis functions and blending weights iteratively [1,2,27]. The benefits of the DEC are also inherited from our network. It is less sensitive to the use of varying hyperparameters and provides state-of-the-art performance for a target application. A variational autoencoder-based deep embedded network is beneficial to high-resolution and non-planar geometry reflectance samples since it does not employ a shape-constrained layer, such as convolutional layers or self-attention modules, on the network.

In summary, the contributions of our research are as follows:

• We proposed Deep embedding clustering-based joint update scheme simultaneously updating non-parametric basis BRDFs and their linear manifold.
• We designed a novel autoencoder that is suited for learning the appearance properties of complex objects using unsupervised learning.
• We demonstrate that our network produces a high-quality rendering result in different illumination conditions.

2. Related Work

2.1. Point-Wise Estimation

The most common way to represent spatially-varying effects over a surface is to treat each surface point as an independent spatial sample. Gardner et al. [6] fitted an isotropic BRDF model at each surface point under a linear light source. Similarly, Aittala et al. [7] used a two specular lobe model to reconstruct the spatially-varying reflectance of a planar object lit by a programmable LCD light source. To simplify the acquisition setup, Dong et al. [31] captured a video sequence in a natural illumination and recovered a data-driven microfacet model at each surface point using a temporal variation in the appearance. Recently, Kang et al. [8] combined a non-linear autoencoder with a near-field acquisition system that emits multiplexing light patterns, in order to efficiently estimate per-point-based anisotropic BRDFs using fewer samples (16∼32 images). All these methods were capable of reconstructing individual BRDFs at each surface point using rich information (e.g., programmed lighting or appearance variation from motion). However, they have not utilized spatial coherency of the surface reflectance which is commonly observed in real materials [11,30,32].

2.2. Estimation Using BRDF Examples

Another class of the SVBRDF estimation method utilizes BRDF examples, called representatives, before manifold mapping in the spatial domain of SVBRDF. These methods form a local linear subspace at every surface point using only the selected representatives that are closed to the observed reflectance. Researchers have proposed various methods to collect representatives: manual acquisition [9,11], NDF synthesis at spatial clusters [32], and the use of a reference chart [10]. Other researchers used data-driven BRDFs (e.g., MERL BRDFs [33]) to jointly optimize the surface normal and reflectance [34,35], or a BRDF dictionary that consists of a set of parametric BRDFs sampled uniformly from a parametric domain. Wu et al. [36] proposed a sparse mixture model of analytic BRDFs to represent Bidirectional Texture Functions (BTFs). Chen et al. [28] utilized a BRDF dictionary with a sparse constraint to reconstruct anisotropic NDFs. Wu et al. [37] used a single parametric BRDF which is best suited for the observed reflectance at each point in order to jointly optimize the geometry, environmental map, and reflectance from an RGBD sequence. Zhou et al. [12] optimized not only blending weights but the number of basis functions blended at a surface point.

Most of the above methods share the same limitation that they rely on various types of given BRDFs. In the case that a prior distribution does not match the appearance of a target object, these methods could fail to produce a proper solution.

2.3. Estimation Using Linear Subspace

Studies in this category seek to use a low-dimensional subspace to represent the spatially-varying reflectance. The linear decomposition methods represent the reflectance at a surface point as a linear combination of basis functions. The basis functions have been constructed by matrix factorization [1,27], or spatial clustering based on the similarity of diffuse albedo [2,4,30,38] and the distance to a cluster center [3,5].

Most methods have fixed the basis functions while forming a linear manifold [3,4,5,30,38] or iteratively updated basis functions and their blending weights [1,2,27]. In comparison to these methods, our method can make further improvements by performing a simultaneous update of both basis NDFs and blending weights.

2.4. Estimation Using Deep Learning

Deep learning-based methods have also been developed to estimate the reflectance properties and they can be broadly categorized into two types: the supervised and the unsupervised. They differ from one another based on the existence of a separate dataset to train the network. Early work in the supervised method reconstructs the intrinsics of Lambertian surface from a collection of real photographs [39,40,41,42]. It is extended to a non-Lambertian surface [17] but their material representation is still based on a simple dichromatic reflection model. For an accurate representation of surface reflectance, many supervised methods have utilized more complex SVBRDFs for inverse rendering. Aittala et al. [43] captured the appearance of a flat surface under a flash light source using Convolutional Neural Network (CNN)-based texture synthesis. Li et al. [13] also recovered the SVBRDF of a flat material that had a homogeneous specular reflection over the surface using CNN-based networks trained by a self-augmentation scheme. Their subsequent work [14] extended it for inexact supervision based on empirical experiments on self-augmentation. Li et al. [24] further refined SVBRDF parameters using learnable non-local smoothness conditions. Deschaintre et al. [15] introduced a rendering loss that compared the appearance of an estimated SVBRDF with the ground truth photographs rendered by in-network rendering layers. Their work had strength in terms of producing a globally consistent solution. Last but not least, Li et al. [18] utilized U-net-based CNNs for estimating the shape and reflectance under multi-bounce reflections. Most supervised methods for SVBRDF estimation rely on strong supervision over the appearance of materials so that they can produce a reliable solution even under an ill-posed condition. However, they limit the range of target application to a near-planar surface [13,15,24,43] or a single-view case [17,18].

Unsupervised learning methods have received less attention in resolving the inverse rendering problem. Kulkarni et al. [44] proposed a variational autoencoder that captured a distribution over the shape and appearance of face images. Tewari et al. [45] employed CNN-based autoencoders for facial reconstruction from a monocular image. Tanini et al. [46] resolved the photometric stereo problem using two CNNs that captured per-point surface normal and reflectance, respectively. Compared to these methods, however, our method is oriented toward an object-specific SVBRDF estimation which can handle multi-view inputs of a 3D object. A general microfacet model that our network trains on yields a more realistic solution of relighting for virtual environment applications.

3. Preliminaries

In this chapter, we describe the terminology and assumptions used in this paper. We assume that a target object is lit by a dominant light source (e.g., photometric stereo [46], mobile flash photograph [2]) and its surface reflectance is isotropic. We do not consider multi-bounce reflections. The irradiance function $L(\mathbf{x},\mathbf{i})$ and geometry of the surface are presumed to be given in advance. A BRDF sample $\tilde{\rho }$ is expressed in a local frame where the upward direction is aligned with the surface normal $\mathbf{n}$ at each $\mathbf{x}$. From the radiance equation introduced under a point light source in [47], $\tilde{\rho }$ can be formulated as

$$\tilde{\rho }(\mathbf{x},\mathbf{i},\mathbf{o})=\frac{I({\mathbf{x}}^{\prime })}{L(\mathbf{x},\mathbf{i})(\mathbf{i}·\mathbf{n})},$$

(1)

where $x^{\prime }$ is the pixel location corresponding to $\mathbf{x}$, and $\mathbf{i}$ and $\mathbf{o}$ are an incident and outgoing vector, respectively.

The surface reflectance is modeled by the general microfacet BRDF [25] as the following:

$$\rho (\mathbf{x},\mathbf{i},\mathbf{o})={\rho }_d(\mathbf{x},\mathbf{i},\mathbf{o})+\frac{D(\mathbf{x},\mathbf{h})G(\mathbf{x},\mathbf{i},\mathbf{o})F(\mathbf{x},\mathbf{i},\mathbf{o})}{4(\mathbf{i}·\mathbf{n})(\mathbf{o}·\mathbf{n})},$$

(2)

where the half vector $\mathbf{h}$ is calculated by the normalized sum of $\mathbf{i}$ and $\mathbf{o}$ as $\mathbf{h}=(\mathbf{i}+\mathbf{o})/\left|\right|\mathbf{i}+\mathbf{o}\left|\right|$.

${\rho }_d(\mathbf{x},\mathbf{i},\mathbf{o})$ is the diffuse term which is a blackbox expression of non-local effects such as sub-surface scattering and inter-reflections. It is simply modeled by the Lambertian surface model: ${\rho }_d(\mathbf{x},\mathbf{i},\mathbf{o})=k_d(\mathbf{x})/\pi$, where $k_d(\mathbf{x})$ is the diffuse coefficient. For isotropic reflectance, the NDF $D(\mathbf{x},\mathbf{h})$ can be expressed as a univariate function of the angle ${\theta }_h$ between $\mathbf{h}$ and $\mathbf{n}$: ${\theta }_h=co{s}^{-1}(\mathbf{h}·\mathbf{n})$. We impose the non-negativity constraint: $D(\mathbf{x},\mathbf{h})\ge 0$ and normalize it so that ${\int }_{{\mathsf{\Omega }}_{+}(n)}(\mathbf{h}·\mathbf{n})D(\mathbf{x},\mathbf{h})d{\omega }_h=1$ where ${\mathsf{\Omega }}_{+}(n)$ is the upper hemispherical domain which satisfies $(\mathbf{h}·\mathbf{n})>0$. For this, we set the specular coefficient $k_s(\mathbf{x})$ as

$$k_s(\mathbf{x})={\int }_{{\mathsf{\Omega }}_{+}(n)}(\mathbf{h}·\mathbf{n})D(\mathbf{x},\mathbf{h})d{\omega }_h.$$

(3)

$G(\mathbf{x},\mathbf{i},\mathbf{o})$ is a geometric term for the shadowing and masking effects by microfacets. We use the uncorrelated geometric term expressed as the product of two independent shadowing functions derived by Ashikmin et al. [48]:

$$G(\mathbf{x},\mathbf{i},\mathbf{o})=S(\mathbf{x},\mathbf{i})S(\mathbf{x},\mathbf{o}),$$

(4)

where

$$S(\mathbf{x},\mathbf{k})=\frac{(\mathbf{k}·\mathbf{h})}{{\int }_{{\mathsf{\Omega }}_{+}(k)\cap {\mathsf{\Omega }}_{+}(n)}(\mathbf{k}·\mathbf{h})D(\mathbf{x},\mathbf{h})d{\omega }_h},$$

(5)

and a given direction $\mathbf{k}$ can be either $\mathbf{i}$ or $\mathbf{o}$ (Figure 1).

$F(\mathbf{x},\mathbf{i},\mathbf{o})$ accounts for the Fresnel effect which is dominant at near-grazing angles. We use the Schlick’s approximation [49] for computational efficiency. It can be represented as a linear function of the refraction index $\eta (\mathbf{x})$ at $\mathbf{x}$ as

$$F(\mathbf{x},\mathbf{i},\mathbf{o})=\eta (\mathbf{x})+(1-\eta (\mathbf{x})){(1-(\mathbf{i}·\mathbf{h}))}^5.$$

(6)

As a result, $\rho (\mathbf{x},\mathbf{i},\mathbf{o})$ can be rewritten as

$$\rho (\mathbf{x},\mathbf{i},\mathbf{o})=\frac{k_d(\mathbf{x})}{\pi }+k_s(\mathbf{x})\frac{D(\mathbf{x},\mathbf{h})S(\mathbf{x},\mathbf{i})S(\mathbf{x},\mathbf{o})F(\mathbf{x},\mathbf{i},\mathbf{o})}{4(\mathbf{i}·\mathbf{n})(\mathbf{o}·\mathbf{n})}.$$

(7)

Our method takes two input tensors I and P constructed from the pairs of an image, the corresponding light source and camera poses. The observation tensor $I\in R^{n_p\times m\times 3}$ contains per-point pixels extracted from multi-view images, where $n_p$ is the number of surface points, m is the number of images. The cosine-weight tensor $P\in R^{n_p\times m\times 4}$ contains four cosine weights $P_{ij}=(\mathbf{i}·\mathbf{n}, \mathbf{o}·\mathbf{n}, \mathbf{h}·\mathbf{n}, \mathbf{i}·\mathbf{h})$ for each $I_{ij}$ where $i=1,2,n_p$ and $j=1,2,m$.

4. Deep Embedding Network

The objective of our network is to reconstruct an observed input image I as close as possible. Our network, the so-called Deep Embedding Network (DEN), does not rely on a prior distribution of the reflectance provided by training data. It instead attempts to estimate the basis BRDFs $B^{\ast }$ represented as a general microfacet model [25], and their manifold from I. These two attributes are simultaneously optimized while training the network through the back-propagation of gradients of the rendering loss.

The design of the DEN is inspired by the deep embedded clustering method [29] which jointly updates a mapping function for a low-dimensional manifold and cluster centroids. The deep embedded clustering is specialized for an image classification problem where its kernel function measures the similarity between low-dimensional points and cluster centroids by an auxiliary target distribution based on the Student’s t-distribution. Since the observed reflectance is not well described by a mixture of Gaussian-like distributions due to the fact that the BRDF is represented as a non-linear function of several BRDF terms described in Equation (7), the DEN employs a different kernel function which measures the weighted reflectance difference [2,32] as the similarity between reflectance at $\mathbf{x}$ and $B^{\ast }$. In order to apply this kernel function to the encoder, we have introduced a new layer, called the Distance Measure (DM) layer as the upfront module of the encoder (Section 4.1). We have also customized our decoder using a single layer, called the Re-Projection (RP) layer, so that it can compute the rendering loss of $B^{\ast }$ and low-dimensional features z embedded in the manifold of $B^{\ast }$ (Section 4.2). In our network design, the weights used for two custom layers DM and RP, which are represented as $B^{\ast }$, are identical. They share the weights while training the network in order to avoid overlapping updates.

Our network architecture is illustrated in Figure 2. The structure of the DEN has single encoder and single decoder analogs with the structure of a variational autoencoder [29,44,45]. Our non-linear encoder as a non-linear mapping function $f_{\mathsf{\Theta }}$ consists of the DM layer and a series of Fully-Connected (FC) layers, where $\mathsf{\Theta }$ indicates learnable parameters of the mapping function f. $f_{\mathsf{\Theta }}$ maps the observation features $I_i$ into the latent space $Z\in R^{n_c}$ where $I=\{I_i\in R^{m\times 3}\}$, $Z=\{z_i\in R^{n_c}\}$, and $n_c$ is the number of basis BRDFs. The single-layer decoder reconstructs $I_i$ from $z_i$ using the rendering equation [47]. In contrast to [44,45], we do not utilize convolutional layers since they impose a strong constraint on the image resolution and are inefficient in handling masked inputs which are common in multi-view images.

4.1. Manifold Mapping

Our encoder learns the linear manifold of the basis BRDFs $B^{\ast }(\mathbf{i},\mathbf{o})$ which are defined as a non-parametric tabulated function that considers three color bands: $B^{\ast }(\mathbf{i},\mathbf{o})\in R^{(n_s+1)\times n_c\times 3}$ where $n_s$ is the number of samples in the NDF domain. The diffuse albedo of each of the basis BRDFs is also listed in the tensor so that there is one additional entry in the first dimension of $B^{\ast }$ [2]. In this paper, we manually set $n_s$ as 90 according to Matusik et al. [33]. We further transform the NDF domain ${\theta }_h$ by ${\theta }_h^{\prime }=\sqrt{{\theta }_h}$ in order to densely sample ${\theta }_h$ near 0 [33].

The reflectance at each $\mathbf{x}$ is represented as the sum of $B^{\ast }(\mathbf{i},\mathbf{o})$ by

$$\rho (\mathbf{x},\mathbf{i},\mathbf{o})=\sum _{c=1}^{n_c}{z}_{ic}{B}_c^{\ast }(\mathbf{i},\mathbf{o}),$$

(8)

where $B_c^{\ast }({\theta }_i,{\theta }_o)$ is a slice of $B^{\ast }$ which represents a single basis BRDF $B_c^{\ast }\in R^{91\times 3}$ and $z_{ic}$ is the corresponding feature in an embedded feature vector $z_i$.

A direct mapping from I into Z [29,44] is undesirable for this case as we mentioned in the above section. Therefore, the DM layer in front of $f_{\mathsf{\Theta }}$ firstly transforms I into a low-dimensional space by measuring the weighted mean squared distance $d_i\in R^{n_c}$ between a partial observation of reflectance at each $\mathbf{x}$ and each $B_c^{\ast }$:

$$d_{ic}=\frac{\sum (B_c^{\ast }(\mathbf{i},\mathbf{o})-\tilde{\rho }({\mathbf{x}}_i,\mathbf{i},\mathbf{o}))(\mathbf{h}·\mathbf{n})}{\sum (\mathbf{h}·\mathbf{n})}.$$

(9)

The following eight FC layers map $d_i$ into Z. We flexibly set the number of nodes in FC layers according to $n_c$ so that each layer has $\{2n_c$, $4n_c$, $8n_c$, $16n_c$, $8n_c$, $4n_c$, $2n_c$, $n_c\}$ nodes, respectively. In this paper, the learnable parameter $\mathsf{\Theta }$ of the mapping function corresponds to the weights of FC layers.

For all FC layers, we apply the ‘Tanh’ activation function except for the last one. In the last layer, the ‘softmax’ activation function is applied so that $z_i$ has non-negative weights for $B^{\ast }$ where ${\sum }_c{z}_{ic}=1$. During the training stage, we set the weight of the DM layer to be non-trainable and copy $B^{\ast }$ from the RP layer to the DM layer after each batch.

4.2. Rendering Loss

The RP layer is a single-layer decoder that computes the rendering loss under a single light source condition similar to [15,46]. We have further customized it to allow for the use of the non-parametric BRDFs $B^{\ast }$.

The rendering loss is defined as

$$L=\left|\right|I({\mathbf{x}}^{\prime })-\rho (\mathbf{x},\mathbf{i},\mathbf{o})L(\mathbf{x},\mathbf{i})(\mathbf{i}·\mathbf{n})\left|\right|$$

(10)

where $\left|\right|·\left|\right|$ indicates the mean squared difference. Gradients for the two major parameters $D^{\ast }$ and $z_i$ are defined as

$$\begin{array}{ll}\hfill & \frac{\partial L}{\partial z_{ic}}=-\frac{2}{m}\sum W_1{W}_2{B}_c^{\ast },\\ \hfill & \frac{\partial L}{\partial B_c^{\ast }}=-\frac{2}{m}\sum W_1{W}_2{z}_{ic}.\end{array}$$

(11)

where $W_1\in R^{m\times 3}$ and $W_2\in R^{m\times 3}$ are weight tensors which can be formulated as

$$\begin{array}{ll}\hfill & W_1(\mathbf{x},\mathbf{i},\mathbf{o})=\frac{k_s(\mathbf{x})S(\mathbf{x},\mathbf{i})S(\mathbf{x},\mathbf{o})F(\mathbf{x},\mathbf{i},\mathbf{o})}{4(\mathbf{i}·\mathbf{n})(\mathbf{o}·\mathbf{n})}\\ & W_2(\mathbf{x},\mathbf{i},\mathbf{o})=\{I({\mathbf{x}}^{\prime })-\rho (\mathbf{x},\mathbf{i},\mathbf{o})L(\mathbf{x},\mathbf{i})(\mathbf{i}·\mathbf{n})\}L(\mathbf{x},\mathbf{i})(\mathbf{i}·\mathbf{n}),\end{array}$$

(12)

4.3. Network Initialization

For the initialization of the DEN, the weights of FC layers are initialized by the ‘glorot’ uniform distribution [50]. To initialize the weights of the DM and RP layers, we firstly performed a manual separation of the diffuse and the specular components for each $\mathbf{x}$ by using a minimum filter [10,32]. We grouped $I_i$ to $n_c$ clusters using a k-means algorithm based on the L2 distance between cluster centroids and $k_d({\mathbf{x}}_{\mathbf{i}})$ in the RGB color space. An initial guess of $B^{\ast }$ was calculated for collected reflectance samples in each cluster. For this, we excluded the samples of near-grazing angles where $cos({\theta }_i)<0.2$ or $cos({\theta }_o)<0.2$ and assumed $F(\mathbf{x},\mathbf{i},\mathbf{o})=1$ [32]. $S(\mathbf{x},\mathbf{i})$ and $S(\mathbf{x},\mathbf{o})$ are also assumed to be equal to 1 at the starting point. After the calculation of $B^{\ast }$ has been performed, the weights of the DM and RP layers are initialized to the value of $B^{\ast }$.

5. Results

5.1. Comparison for the Reconstruction Quality

We validated our network on 3D objects (Buddha, Minotaur, and Terracotta) provided by Schwartz et al. [51]. This 3D dataset aims to estimate a dense BTF of objects which have complex shapes and surface reflectance so that they captured $151\times 151=22,801$ sample images. For 3D objects, we parameterized the surface of a triangle mesh into a 2D texture map using the ABF++ mesh parameterization method [52]. Each 2D texture map is rasterized in the resolution of $1024\times 1024$. To construct the input tensors I and P, we selected the co-located images, where the camera and light source positions are identical [34].

We compared the DEN with the Discrete Spatial Clustering (DSC) [4] and the Log-Mapping PCA (LMPCA) [53] method. The DSC fits a parametric BRDF to reflectance data collected in each cluster. In the comparison, we used the Cook–Torrance [25] BRDF with a parametric NDF for DSC and LMPCA.

Table 1. PSNR comparison with DEN, DSC, and LMPCA. We evaluated the PSNR of each reconstruction result with respect to the ground truth input tensor I.

Object	PSNR
	DEN	DSC [4]	LMPCA [53]
Buddha	31.5	29.7	30.0
Minotaur	36.9	31.8	32.4
Terracotta	40.8	34.5	35.4

shows the PSNR of reconstruction results with respect to the ground truth. Overall, the DEN outperforms the competitive methods. This improvement is derived from the mechanism of the DEN that can learn non-parametric NDFs with its blending weights in the spatial domain. Since the non-parametric NDF is capable of representing a comprehensive surface reflection by giving more degrees of freedom in the shape of specular reflection, the reconstruction result of the DEN is improved in comparison with the method using a parametric BRDF [4] or a pre-defined BRDF [53].

Figure 3 shows the visual comparison of 3D objects. In contrast to the DSC which fails to capture underlying surface reflectance including an irregular mixture of materials, the DEN robustly reproduced the surface reflectance. Meanwhile, the result of the LMPCA seems to be over-saturated in specular regions.

To observe whether the DEN is over-fitted to training data, we evaluate the RMSE of test data that has a novel view and illumination conditions. The result in

Table 2. RMS errors of the DEN for the training data and the test data in the BTF dataset. The number of basis BRDFs $n_c$ are also listed in the table.

Object	${\mathit{n}}_{\mathit{c}}$	RMSE of Training Data	RMSE of Test Data
Buddha	9	0.0264	0.0274
Minotaur	10	0.0143	0.0141
Terracotta	9	0.0091	0.0090

indicates that the SVBRDF estimated from the DEN is not over-fitted and well applied to different views and illumination conditions from that of images in training data. This characteristic is very important to guarantee visual consistency in real-time rendering (Section 5.7).

5.2. Comparison with the Iterative Optimization Method

Iterative optimization methods which separately optimize the basis functions of the SVBRDF and the blending weights have been widely used [1,2,27,28]. We compared the convergence speed with that of the iterative optimization method. For comparison, we implemented a quadratic programming-based optimizer that updates $B^{\ast }$ and $z_i$ by iteratively solving two quadratic equations in Equation (10). Refer to Nam et al. [2] for the details of constructing these quadratic equations. While training the iterative method, we used the same constraints imposed on the DEN. Figure 4 shows RMS errors in terms of the computation time. Our method converged faster than the iterative optimization due to the simultaneous update of $B^{\ast }$ and $z_i$. Nevertheless, our method shows a good visual quality as shown in Figure 4.

5.3. Gradient Observation

Figure 5 shows the histogram of the magnitude of the gradients $\left|\frac{\partial L}{\partial B_c^{\ast }}\right|$ according to $|z_{i,c}|$ at the first epoch. As noted in Equation (11), $\left|\frac{\partial L}{\partial B_c^{\ast }}\right|$ is linearly proportional to $|z_{i,c}|$. It indicates that $B_c^{\ast }$ which is closer to a partial observation $\tilde{B}(\mathbf{x},{\theta }_h)$ has been updated more rapidly. This characteristic plays an important role in the convergence of the DEN. We observed that a random initialization of $B^{\ast }$ leads to local minima in which the DEN gave a poor result (Figure 4(left)). However, when an initial value of $B^{\ast }$ computed by a discrete spatial clustering [2,3,5,38] is given to the DEN, it outperforms the iterative optimization method for $B^{\ast }$ and $z_i$.

5.4. Comparison of Photometric Stereo Data

The DiLiGenT benchmark dataset (Shi et al. [54]) provides 96 photometric stereo images and the corresponding light source positions for 10 objects, as well as their ground truth surface normal. They also provide the BRDF fitting results of Alldrin et al. [27] under the given surface normal and the given number of basis functions as three.

Table 3. Comparison of PSNR with ACLS for the “DiLigenT” dataset. We generated the reconstruction results using the ground truth surface normal for both methods.

Object	PSNR
	ACLS [27]	DEC
Ball	38.9	43.3
Bear	43.1	41.4
Buddha	38.2	39.2
Cat	43.1	43.3
Cow	40.3	39.8
Goblet	36.7	35.7
Harvest	30.4	30.8
Pot1	45.8	48.9
Pot2	45.6	49.2
Reading	27.5	29.0

summarizes the comparison result in terms of the PSNR. Our method shows a better performance than the ACLS [27]. Although the PSNR in several objects is marginally improved by the proposed method, we have observed that the DEN significantly improves the visual quality of the reconstruction. Figure 6 shows highlighted results for three objects: Harvest, Reading, and Ball, to compare the visual quality of the two methods. The DEN produces more realistic solutions than the competitive method in both specular and diffuse regions.

5.5. Sensitivity for Normal Error

The reconstruction quality of the DEN can be affected by the noise in the surface normal since it relies on the given surface geometry and camera configurations. We measured the sensitivity of the reconstruction results in terms of the normal error of the 10 objects for the DiLiGenT dataset. For each object, we constructed 11 cosine-weight tensors P corresponding to a single observation tensor I by adding uniform random noise to angles of the surface normal in the polar coordinates. We gradually increased the amplitude of noise $\mu$ as $\mu =\{0,1,2,\dots ,9,10\}$°. As shown in Figure 7, the RMS error of reconstruction results is increased as $\mu$ increases. Shiny objects tend to be more sensitive to normal errors as reported by Zhou et al. [12].

5.6. Selection of $n_c$

The number of basis functions $n_c$ is the most dominant hyper-parameter of the DEN. Similar to a grid hyperparameter tuning scheme for linear manifold search [55], we empirically select it by a brute-force search [1,2,10,27]. Figure 8 shows the tendency of the RMS errors according to $n_c$. In our experiments, a small number of basis functions is enough to represent the reflectance of many surface points (300∼500 k). The reconstruction quality is insignificantly improved when $n_c>5$.

5.7. Relighting Result

Figure 9 shows the relighting result under various environmental maps. We applied the final weight of the RP layer as the basis NDFs and the final output of the encoder as the weight of the basis NDFs. As shown in Figure 9, our network can generate a high-fidelity reconstruction in given view and illumination conditions.

6. Conclusions

In this paper, we have presented a novel Deep Embedding Network (DEN) for the simultaneous update of the basis NDFs and their linear manifold via the dual gradient (Equation (10)) in order to search for a photo-realistic representation of an opaque object using SVBRDF. We have empirically shown that the basis NDFs and their blending weights are linear-proportionally updated in our autoencoder network. We also demonstrated that our scheme excels in optimizing the rendering loss in terms of the convergence speed and the visual quality compared to the iterative SVBRDF update methods [2,28].

Our method is specialized to model intra-class variations of the surface reflectance. It is advantageous for restoring the high-definition appearance of a 3D object measured in a laboratory environment. However, our method does not take into account a sparse measurement [15,18,43], or unknown illumination conditions [16,37]. In future work, we plan to extend our work to estimate not only the surface reflectance but the surface geometry [56]. This could be achieved by extending our network to jointly estimate the surface normal and the reflectance as similar to Taniai et al. [46] or Nam et al. [2]. To achieve the goal, we can directly adopt the CNN-based reflectance estimator [46] and the iterative quadratic solver [2].