We present in this paper a superpixel segmentation algorithm called Linear Spectral Clustering (LSC), which produces compact and uniform superpixels with low computational costs. Basically, normalized cuts formulation of the is adopted based on similarity metric that measures color space proximity between image pixels. However, instead using traditional eigen-based algorithm, we approximate kernel function leading to an explicitly mapping pixel values coordinates into high dimensional...

Reconstructing shape and reflectance properties from images is a highly under-constrained problem, has previously been addressed by using specialized hardware to capture calibrated data or assuming known (or constrained) reflectance. In contrast, we demonstrate that can recover non-Lambertian, spatially-varying BRDFs complex geometry belonging any arbitrary class, single RGB image captured under combination of unknown environment illumination flash lighting. We achieve this training deep...

We propose a deep inverse rendering framework for indoor scenes. From single RGB image of an arbitrary scene, we obtain complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel network incorporates physical insights -- including spherical Gaussian lighting representation, differentiable layer to model appearance, cascade structure iteratively refine the predictions bilateral solver refinement allowing us...

In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images low computational costs. LSC, normalized cuts-based formulation image adopted using distance metric that measures the color similarity space proximity between pixels. However, rather than directly traditional eigen-based algorithm, approximate through deliberately designed...

Attention based automatic image cropping aims at preserving the most visually important region in an image. A common task this kind of method is to search for smallest rectangle inside which summed attention maximized. We demonstrate that under appropriate formulations, can be achieved using efficient algorithms with low computational complexity. In a practically useful scenario where aspect ratio given, problem solved complexity linear number pixels. also study possibility multiple and new...

Recovering the 3D shape of transparent objects using a small number unconstrained natural images is an ill-posed problem. Complex light paths induced by refraction and reflection have prevented both traditional deep multiview stereo from solving this challenge. We propose physically-based network to recover few acquired with mobile phone camera, under known but arbitrary environment map. Our novel contributions include normal representation that enables model complex transport through local...

We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, transforming scans into high-quality appearance, layout, semantic labels, high quality spatially-varying BRDF complex lighting, including direct, indirect visibility components. This enables important applications in inverse rendering, scene understanding robotics. show...

Indoor scenes exhibit significant appearance variations due to myriad interactions between arbitrarily diverse object shapes, spatially-changing materials, and complex lighting. Shadows, highlights, inter-reflections caused by visible invisible light sources require reasoning about long-range for inverse rendering, which seeks recover the components of image formation, namely, shape, material, In this work, our intuition is that attention learned transformer architectures ideally suited...

Considering the four characteristics of strains, including acid production, tolerance, salt and nitrite degradation rate, Pediococcus pentosaceus NCU006063 was selected as fermentation agent, medium composition optimized using Plackett–Burman central composite rotational design. Three seven factors studied in design significantly affected viable counts. A used to optimize significant generate response surface plots. Using these plots point predictions, optimal were soy peptone (38.75 g/L),...

Background The fermentation characteristics of cigar tobacco leaves are closely influenced by the bacterial strains present during process. This study aims to explore relationship between communities and flavor, as well impact key species on overall quality cigars. Result results showed that Staphylococcus nepalensis was dominant bacteria Correlations flavor revealed positively correlated with carotenoid degradation products, indicating its potential role in promoting formation. Compared...

Most indoor 3D scene reconstruction methods focus on recovering geometry and layout. In this work, we go beyond to propose PhotoScene <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://github.com/ViLab-UCSD/PhotoScene, a framework that takes input image(s) of along with approximately aligned CAD (either reconstructed automatically or manually specified) builds photorealistic digital twin high-quality materials similar...

Reconstructing the shape and spatially varying surface appearances of a physical-world object as well its surrounding illumination based on 2D images (e.g., photographs) has been long-standing problem in computer vision graphics. In this paper, we introduce an accurate highly efficient reconstruction pipeline combining neural physics-based inverse rendering (PBIR). Our firstly leverages SDF to produce high-quality but potentially imperfect shape. Then, material lighting distillation stage...

Imaging through dynamic refractive media, such as looking into turbulent water, or hot air, is challenging since light rays are bent by unknown amounts leading to complex geometric distortions. Inverting these distortions and recovering high quality images an inherently ill-posed problem, previous works require extra information frame-rate video a template image, which limits their applicability in practice. This paper proposes training deep convolution neural network undistort effects using...

The appearance of a transparent object is determined by combination refraction and reflection, as governed complex function its shape well the surrounding environment. Prior works on 3D reconstruction have largely ignored objects due to this challenge, yet they occur frequently in real-world scenes. This paper presents an approach estimate depths normals for using single image acquired under distant but otherwise arbitrary environment map. In particular, we use deep convolutional neural...

We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, transforming scans into high-quality appearance, layout, semantic labels, high quality spatially-varying BRDF complex lighting, including direct, indirect visibility components. This enables important applications in inverse rendering, scene understanding robotics. show...

Highly effective optimization frameworks have been developed for traditional multiview stereo relying on lambertian photoconsistency. However, they do not account complex material properties. On the other hand, recent works explored PDE invariants shape recovery with BRDFs, but incorporated into robust numerical frameworks. We present a variational energy minimization framework of in complex, unknown BRDFs. While our formulation is general, we demonstrate its efficacy using single light...

We propose a material acquisition approach to recover the spatially-varying BRDF and normal map of near-planar surface from single image captured by handheld mobile phone camera. Our method images under arbitrary environment lighting with flash turned on, thereby avoiding shadows while simultaneously capturing high-frequency specular highlights. train CNN regress an SVBRDF normals this image. network is trained using large-scale dataset designed incorporate physical insights for estimation,...

We propose a physically motivated deep learning framework to solve general version of the challenging indoor lighting estimation problem. Given single LDR image with depth map, our method predicts spatially consistent at any given position. Particularly, when input is an video sequence, not only progressively refines prediction as it sees more regions, but also preserves temporal consistency by keeping refinement smooth. Our reconstructs spherical Gaussian volume (SGLV) through tailored 3D...

We present a method searching for the main symmetric axis in an image based on SAX representation which converts pixels to symbols and classical linear time palindrome detecting algorithm. This generates curve outlining by dynamic programming produces straight RANSAC, fitting tolerates outliers. The computational complexity is O(mn) m×n image, pixel number. can be extended multiple axes detection. Comparing with state-of-the-art symmetry detection methods, our has comparable precision much faster.

We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored monocular video. Building upon the efficiency of Gaussian splatting, our approach extends representation to accommodate elements via set residing in canonical space, and time-dependent deformation field defined by multi-layer perceptron (MLP). Moreover, under assumption most natural scenes have large regions remain static, we allow MLP focus its representational power additionally including...

We propose a deep inverse rendering framework for indoor scenes. From single RGB image of an arbitrary scene, we create complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. To train this network, augment the SUNCG dataset with real-world materials render them fast, high-quality, physically-based GPU renderer to large-scale, photorealistic dataset. Our network incorporates physical insights -- including...