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 time-comsuming. Recent strategies have adopted 3D Gaussian Splatting (3D-GS) for inverse rendering, which have led to quick and effective outcomes. However, these techniques generally have difficulty in producing believable geometries and materials for glossy objects, a challenge that stems from the inherent ambiguities of inverse rendering. To address this, we introduce GlossyGS, an innovative 3D-GS-based inverse rendering framework that aims to precisely reconstruct the geometry and materials of glossy objects by integrating material priors. The key idea is the use of micro-facet geometry segmentation prior, which helps to reduce the intrinsic ambiguities and improve the decomposition of geometries and materials. Additionally, we introduce a normal map prefiltering strategy to more accurately simulate the normal distribution of reflective surfaces. These strategies are integrated into a hybrid geometry and material representation that employs both explicit and implicit methods to depict glossy objects. We demonstrate through quantitative analysis and qualitative visualization that the proposed method is effective to reconstruct high-fidelity geometries and materials of glossy objects, and performs favorably against State-of-the-Arts.

Material appearance is a key component of photorealism, with a pronounced impact on human perception. Although there are many prior works targeting at measuring opaque materials using light-weight setups (e.g., consumer-level cameras), little attention is paid on acquiring the optical properties of translucent materials which are also quite common in nature. In this paper, we present a practical method for acquiring scattering properties of translucent materials, based solely on ordinary images captured with unknown lighting and camera parameters. The key to our method is an inter-pixel translucency prior which states that image pixels of a given homogeneous translucent material typically form curves (dubbed translucent curves) in the RGB space, of which the shapes are determined by the parameters of the material. We leverage this prior in a specially-designed convolutional neural network comprising multiple encoders, a translucency-aware feature fusion module and a cascaded decoder. We demonstrate, through both visual comparisons and quantitative evaluations, that high accuracy can be achieved on a wide range of real-world translucent materials.

Depth estimation is a fundamental task in many vision applications. With the popularity of omnidirectional cameras, it becomes a new trend to tackle this problem in the spherical space. In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-view, providing much more complete descriptions of the scene than perspective images. However, fully-convolutional networks that most current solutions rely on fail to capture rich global contexts from the panorama. To address this issue and also the distortion of equirectangular projection in the panorama, we propose Cubemap Vision Transformers (CViT), a new transformer-based architecture that can model long-range dependencies and extract distortion-free global features from the panorama. We show that cubemap vision transformers have a global receptive field at every stage and can provide globally coherent predictions for spherical signals. As a general architecture, it removes any restriction that has been imposed on the panorama in many other monocular panoramic depth estimation methods. To preserve important local features, we further design a convolution-based branch in our pipeline (dubbed GLPanoDepth) and fuse global features from cubemap vision transformers at multiple scales. This global-to-local strategy allows us to fully exploit useful global and local features in the panorama, achieving state-of-the-art performance in panoramic depth estimation.

Abstract The problem of reconstructing spatially‐varying BRDFs from RGB images has been studied for decades. Researchers found themselves in a dilemma: opting for either higher quality with the inconvenience of camera and light calibration, or greater convenience at the expense of compromised quality without complex setups. We address this challenge by introducing a two‐branch network to learn the lighting effects in images. The two branches, referred to as Light‐known and Light‐aware, diverge in their need for light information. The Light‐aware branch is guided by the Light‐known branch to acquire the knowledge of discerning light effects and surface reflectance properties, but without the reliance of light positions. Both branches are trained using the synthetic dataset, but during testing on real‐world cases without calibration, only the Light‐aware branch is activated. To facilitate a more effective utilization of various light conditions, we employ gated recurrent units (GRUs) to fuse the features extracted from different images. The two modules mutually benefit when multiple inputs are provided. We present our reconstructed results on both synthetic and real‐world examples, demonstrating high quality while maintaining a lightweight characteristic in comparison to previous methods.

Existing convolutional neural networks have achieved great success in recovering Spatially Varying Bidirectional Surface Reflectance Distribution Function (SVBRDF) maps from a single image. However, they mainly focus on handling low-resolution (e.g., 256 × 256) inputs. Ultra-High Resolution (UHR) material maps are notoriously difficult to acquire by existing networks because (1) finite computational resources set bounds for input receptive fields and output resolutions, and (2) convolutional layers operate locally and lack the ability to capture long-range structural dependencies in UHR images. We propose an implicit neural reflectance model and a divide-and-conquer solution to address these two challenges simultaneously. We first crop a UHR image into low-resolution patches, each of which are processed by a local feature extractor to extract important details. To fully exploit long-range spatial dependency and ensure global coherency, we incorporate a global feature extractor and several coordinate-aware feature assembly modules into our pipeline. The global feature extractor contains several lightweight material vision transformers that have a global receptive field at each scale and have the ability to infer long-term relationships in the material. After decoding globally coherent feature maps assembled by coordinate-aware feature assembly modules, the proposed end-to-end method is able to generate UHR SVBRDF maps from a single image with fine spatial details and consistent global structures.

This paper addresses the task of estimating spatially-varying reflectance (i.e., SVBRDF) from a single, casually captured image. Central to our method is a highlight-aware (HA) convolution operation and a two-stream neural network equipped with proper training losses. Our HA convolution, as a novel variant of standard (ST) convolution, directly modulates convolution kernels under the guidance of automatically learned masks representing potentially overexposed highlight regions. It helps to reduce the impact of strong specular highlights on diffuse components and at the same time, hallucinates plausible contents in saturated regions. Considering that variation of saturated pixels also contains important cues for inferring surface bumpiness and specular components, we design a two-stream network to extract features from two different branches stacked by HA convolutions and ST convolutions, respectively. These two groups of features are further fused in an attention-based manner to facilitate feature selection of each SVBRDF map. The whole network is trained end to end with a new perceptual adversarial loss which is particularly useful for enhancing the texture details. Such a design also allows the recovered material maps to be disentangled. We demonstrate through quantitative analysis and qualitative visualization that the proposed method is effective to recover clear SVBRDFs from a single casually captured image, and performs favorably against state-of-the-arts. Since we impose very few constraints on the capture process, even a non-expert user can create high-quality SVBRDFs that cater to many graphical applications.