Volume 44 (2025)

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https://diglib.eg.org/handle/10.2312/3607127

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2D Neural Fields with Learned Discontinuities
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Liu, Chenxi; Wang, Siqi; Fisher, Matthew; Aneja, Deepali; Jacobson, Alec; Bousseau, Adrien; Day, Angela
Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity, respectively. Current neural fields offer high fidelity and resolution independence but require predefined meshes with known discontinuities, restricting their utility. We observe that by treating all mesh edges as potential discontinuities, we can represent the discontinuity magnitudes as continuous variables and optimize. We further introduce a novel discontinuous neural field model that jointly approximates the target image and recovers discontinuities. Through systematic evaluations, our neural field outperforms other methods that fit unknown discontinuities with discontinuous representations, exceeding Field of Junction and Boundary Attention by over 11dB in both denoising and super-resolution tasks and achieving 3.5× smaller Chamfer distances than Mumford-Shah-based methods. It also surpasses InstantNGP with improvements of more than 5dB (denoising) and 10dB (super-resolution). Additionally, our approach shows remarkable capability in approximating complex artistic and natural images and cleaning up diffusion-generated depth maps.
3DGM: Deformable and Texturable 3D Gaussian Model via Level-of-Detail Proxy
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Xiangzhi Eric; Sin, Zackary P. T.; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
3D Gaussian Splatting has markedly impacted neural rendering by achieving impressive fidelity and performance. Despite this achievement, it is not readily applicable to developing interactive applications. Real-time applications like XR apps and games require functions such as animation, UV mapping and level of detail (LOD) simultaneously manipulated through a 3D model. To address this need, we propose a modelling strategy analogous to typical 3D models, which we call 3D Gaussian Model (3DGM). 3DGM relies on attaching 3D Gaussians on the triangles of a mesh proxy, and the key idea is to bind sheared 3D Gaussians in texture space and re-projecting them back to world space through implicit shell mapping; this design naturally enables deformation and UV mapping via the proxy. Further, to optimize speed and fidelity based on different viewing distances, each triangle can be tessellated to change the number of involved 3D Gaussians adaptively. Application-wise, we will show that our proxy-based 3DGM is capable of enabling novel deformation without animated training data, texture transferring via UV mapping of the 3D Gaussians, and LOD rendering. The results indicate that our model achieves better fidelity for deformation and better optimization of fidelity and performance given different viewing distances. Further, we believe the results indicate the potential of our work for enabling interactive applications for 3D Gaussian Splatting.
4-LEGS: 4D Language Embedded Gaussian Splatting
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Fiebelman, Gal; Cohen, Tamir; Morgenstern, Ayellet; Hedman, Peter; Averbuch-Elor, Hadar; Bousseau, Adrien; Day, Angela
The emergence of neural representations has revolutionized our means for digitally viewing a wide range of 3D scenes, enabling the synthesis of photorealistic images rendered from novel views. Recently, several techniques have been proposed for connecting these low-level representations with the high-level semantics understanding embodied within the scene. These methods elevate the rich semantic understanding from 2D imagery to 3D representations, distilling high-dimensional spatial features onto 3D space. In our work, we are interested in connecting language with a dynamic modeling of the world. We show how to lift spatio-temporal features to a 4D representation based on 3D Gaussian Splatting. This enables an interactive interface where the user can spatiotemporally localize events in the video from text prompts. We demonstrate our system on public 3D video datasets of people and animals performing various actions.
Accelerating Signed Distance Functions
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hubert-Brierre, Pierre; Guérin, Eric; Peytavie, Adrien; Galin, Eric; Christie, Marc; Pietroni, Nico; Wang, Yu-Shuen
Processing and particularly visualizing implicit surfaces remains computationally intensive when dealing with complex objects built from construction trees. We introduce optimization nodes to reduce the computational cost of the field function evaluation for hierarchical construction trees, while preserving the Lipschitz or conservative properties of the function. Our goal is to propose acceleration nodes directly embedded in the construction tree, and avoid external, accompanying data-structures such as octrees. We present proxy and continuous level of detail nodes to reduce the overall evaluation cost, along with a normal warping technique that enhances surface details with negligible computational overhead. Our approach is compatible with existing algorithms that aim at reducing the number of function calls. We validate our methods by computing timings as well as the average cost for traversing the tree and evaluating the signed distance field at a given point in space. Our method speeds-up signed distance field evaluation by up to three orders or magnitude, and applies both to ray-surface intersection computation in Sphere Tracing applications, and to polygonization algorithms.
Accessible Text Descriptions for UpSet Plots
(The Eurographics Association and John Wiley & Sons Ltd., 2025) McNutt, Andrew; McCracken, Maggie K.; Eliza, Ishrat Jahan; Hajas, Daniel; Wagoner, Jake; Lanza, Nate; Wilburn, Jack; Creem-Regehr, Sarah; Lex, Alexander; Aigner, Wolfgang; Andrienko, Natalia; Wang, Bei
Data visualizations are typically not accessible to blind and low-vision (BLV) users. Automatically generating text descriptions offers an enticing mechanism for democratizing access to the information held in complex scientific charts, yet appropriate procedures for generating those texts remain elusive. Pursuing this issue, we study a single complex chart form: UpSet plots. UpSet Plots are a common way to analyze set data, an area largely unexplored by prior accessibility literature. By analyzing the patterns present in real-world examples, we develop a system for automatically captioning any UpSet plot. We evaluated the utility of our captions via semi-structured interviews with (N=11) BLV users and found that BLV users find them informative. In extensions, we find that sighted users can use our texts similarly to UpSet plots and that they are better than naive LLM usage.
Adaptive and Iterative Point Cloud Denoising with Score-Based Diffusion Model
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Zhaonan; Li, Manyi; Xin, Shiqing; Tu, Changhe; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
Point cloud denoising task aims to recover the clean point cloud from the scanned data coupled with different levels or patterns of noise. The recent state-of-the-art methods often train deep neural networks to update the point locations towards the clean point cloud, and empirically repeat the denoising process several times in order to obtain the denoised results. It is not clear how to efficiently arrange the iterative denoising processes to deal with different levels or patterns of noise. In this paper, we propose an adaptive and iterative point cloud denoising method based on the score-based diffusion model. For a given noisy point cloud, we first estimate the noise variation and determine an adaptive denoising schedule with appropriate step sizes, then invoke the trained network iteratively to update point clouds following the adaptive schedule. To facilitate this adaptive and iterative denoising process, we design the network architecture and a two-stage sampling strategy for the network training to enable feature fusion and gradient fusion for iterative denoising. Compared to the state-of-the-art point cloud denoising methods, our approach obtains clean and smooth denoised point clouds, while preserving the shape boundary and details better. Our results not only outperform the other methods both qualitatively and quantitatively, but also are preferable on the synthetic dataset with different patterns of noises, as well as the real-scanned dataset.
Adaptive Multi-view Radiance Caching for Heterogeneous Participating Media
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Stadlbauer, Pascal; Tatzgern, Wolfgang; Mueller, Joerg H.; Winter, Martin; Stojanovic, Robert; Weinrauch, Alexander; Steinberger, Markus; Bousseau, Adrien; Day, Angela
Achieving lifelike atmospheric effects, such as fog, is essential in creating immersive environments and poses a formidable challenge in real-time rendering. Highly realistic rendering of complex lighting interacting with dynamic fog can be very resourceintensive, due to light bouncing through a complex participating media multiple times. We propose an approach that uses a multi-layered spherical harmonics probe grid to share computations temporarily. In addition, this world-space storage enables the sharing of radiance data between multiple viewers. In the context of cloud rendering this means faster rendering and a significant enhancement in overall rendering quality with efficient resource utilization.
The Affine Heat Method
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Soliman, Yousuf; Sharp, Nicholas; Attene, Marco; Sellán, Silvia
This work presents the Affine Heat Method for computing logarithmic maps. These maps are local surface parameterizations defined by the direction and distance along shortest geodesic paths from a given source point, and arise in many geometric tasks from local texture mapping to geodesic distance-based optimization. Our main insight is to define a connection Laplacian with a homogeneous coordinate accounting for the translation between tangent coordinate frames; the action of short-time heat flow under this Laplacian gives both the direction and distance from the source, along shortest geodesics. The resulting numerical method is straightforward to implement, fast, and improves accuracy compared to past approaches. We present two variants of the method, one of which enables pre-computation for fast repeated solves, while the other resolves the map even near the cut locus in high detail. As with prior heat methods, our approach can be applied in any dimension and to any spatial discretization, including polygonal meshes and point clouds, which we demonstrate along with applications of the method.
AI-ChartParser: A Method For Extracting Experimental Data From Curve Charts in Academic Papers
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Yang, Wenjin; He, Jie; Zhang, Xiaotong; Gong, Haiyan; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
In the fields of engineering and natural sciences, curve charts serve as indispensable visualization tools for scientific research, product development and engineering design, as they encapsulate crucial data necessary for comprehensive analysis. Existing methodologies for data extraction from line charts predominantly depend on single-task models, which frequently exhibit limitations in efficiency and generalization. To overcome these challenges, we propose AI-ChartParser, an end-to-end deep learning model that employs multi-task learning to concurrently execute chart element detection, pivot point detection and curve detection. This approach effectively and efficiently parses diverse chart formats within a cohesive framework. Furthermore, we introduce an Interval-Mean Space-Numerical Mapping algorithm designed to address challenges in data range extraction, thereby significantly minimizing conversion errors. We have incorporated all the methodologies discussed in this paper to develop a comprehensive data extraction tool, facilitating the automatic conversion of line charts into tabular data. Our model exhibits exceptional performance on complex real-world datasets, achieving state-of-the-art accuracy and speed across all three tasks. To facilitate further research, the source codes and pre-trained models are released at https://github.com/ywking/ChartParser.git.
All-frequency Full-body Human Image Relighting
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Tajima, Daichi; Kanamori, Yoshihiro; Endo, Yuki; Bousseau, Adrien; Day, Angela
Relighting of human images enables post-photography editing of lighting effects in portraits. The current mainstream approach uses neural networks to approximate lighting effects without explicitly accounting for the principle of physical shading. As a result, it often has difficulty representing high-frequency shadows and shading. In this paper, we propose a two-stage relighting method that can reproduce physically-based shadows and shading from low to high frequencies. The key idea is to approximate an environment light source with a set of a fixed number of area light sources. The first stage employs supervised inverse rendering from a single image using neural networks and calculates physically-based shading. The second stage then calculates shadow for each area light and sums up to render the final image. We propose to make soft shadow mapping differentiable for the area-light approximation of environment lighting. We demonstrate that our method can plausibly reproduce all-frequency shadows and shading caused by environment illumination, which have been difficult to reproduce using existing methods.
Anisotropic Gauss Reconstruction and Global Orientation with Octree-based Acceleration
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Ma, Yueji; Shen, Jialu; Meng, Yanzun; Xiao, Dong; Shi, Zuoqiang; Wang, Bin; Attene, Marco; Sellán, Silvia
Unoriented surface reconstruction is an important task in computer graphics. Recently, methods based on the Gauss formula or winding number have achieved state-of-the-art performance in both orientation and surface reconstruction. The Gauss formula or winding number, derived from the fundamental solution of the Laplace equation, initially found applications in calculating potentials in electromagnetism. Inspired by the practical necessity of calculating potentials in diverse electromagnetic media, we consider the anisotropic Laplace equation to derive the anisotropic Gauss formula and apply it to surface reconstruction, called ''anisotropic Gauss reconstruction''. By leveraging the flexibility of anisotropic coefficients, additional constraints can be introduced to the indicator function. This results in a stable linear system, eliminating the need for any artificial regularization. In addition, the oriented normals can be refined by computing the gradient of the indicator function, ultimately producing high-quality normals and surfaces. Regarding the space/time complexity, we propose an octree-based acceleration algorithm to achieve a space complexity of O(N) and a time complexity of O(NlogN). Our method can reconstruct ultra-large-scale models (exceeding 5 million points) within 4 minutes on an NVIDIA RTX 4090 GPU. Extensive experiments demonstrate that our method achieves state-of-the-art performance in both orientation and reconstruction, particularly for models with thin structures, small holes, or high genus. Both CuPy-based and CUDA-accelerated implementations are made publicly available at https://github.com/mayueji/AGR.
Approximating Procedural Models of 3D Shapes with Neural Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hossain, Ishtiaque; Shen, I-Chao; Kaick, Oliver van; Bousseau, Adrien; Day, Angela
Procedural modeling is a popular technique for 3D content creation and offers a number of advantages over alternative techniques for modeling 3D shapes. However, given a procedural model, predicting the procedural parameters of existing data provided in different modalities can be challenging. This is because the data may be in a different representation than the one generated by the procedural model, and procedural models are usually not invertible, nor are they differentiable. In this paper, we address these limitations and introduce an invertible and differentiable representation for procedural models. We approximate parameterized procedures with a neural network architecture NNProc that learns both the forward and inverse mapping of the procedural model by aligning the latent spaces of shape parameters and shapes. The network is trained in a manner that is agnostic to the inner workings of the procedural model, implying that models implemented in different languages or systems can be used. We demonstrate how the proposed representation can be used for both forward and inverse procedural modeling. Moreover, we show how NNProc can be used in conjunction with optimization for applications such as shape reconstruction from an image or a 3D Gaussian Splatting.
Arches: A Cycle-Level Hardware Simulation Framework for Exploring Massively Parallel Ray Tracing Architectures
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Haydel, Jacob; Bhokare, Gaurav; Zeng, Kunnong; Hong, Pengpei; Kondguli, Sushant; Budge, Brian; Brunvand, Erik; Yuksel, Cem; Knoll, Aaron; Peters, Christoph
We introduce Arches, a hardware simulation framework designed to explore and evaluate massively parallel ray-tracing architectures. Operating at the cycle level, Arches captures detailed performance metrics, including computational throughput, onchip data movement across processors, caches, and off-chip communication via an accurate memory system model. The framework is modular, allowing flexible configuration and interconnection of processor cores, caches, and custom hardware units, enabling easy exploration of diverse hardware architectures. Arches supports high-performance parallel execution, simulating complex ray tracing workloads to image completion. It leverages the GNU toolchain, allowing users to write C++ software targeting both the simulated architecture and native execution for debugging, including support for custom instructions to control specialized hardware components. The framework provides comprehensive performance instrumentation, offering insights into time-varying statistics across all modules and identifying performance bottlenecks. Our evaluations demonstrate that Arches delivers performance estimates closely matching real hardware, offering faster and more accurate simulations than existing open-source hardware simulators. Its modularity also makes it a valuable tool for exploring alternative parallel computing strategies for high-performance ray tracing, and its extensibility enables adaptation for other workloads or general-purpose computation.
Arrange and Traverse Algorithm for Computation of Reeb Spaces of Piecewise Linear Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hristov, Petar; Sakurai, Daisuke; Carr, Hamish; Hotz, Ingrid; Masood, Talha Bin; Attene, Marco; Sellán, Silvia
We present the first combinatorial algorithm for efficiently computing the Reeb space in all dimensions. The Reeb space is a higher-dimensional generalization of the Reeb graph, which is standard practice in the analysis of scalar fields, along with other computational topology tools such as persistent homology and the Morse-Smale complex. One significant limitation of topological tools for scalar fields is that data often involves multiple variables, where joint analysis is more insightful. Generalizing topological data structures to multivariate data has proven challenging and the Reeb space is one of the few available options. However, none of the existing algorithms can efficiently compute the Reeb space in arbitrary dimensions and there are no available implementations which are robust with respect to numerical errors. We propose a new algorithm for computing the Reeb space of a generic piecewise linear map over a simplicial mesh of any dimension called arrange and traverse. We implement a robust specialization of our algorithm for tetrahedral meshes and evaluate it on real-life data.
Artist-Inator: Text-based, Gloss-aware Non-photorealistic Stylization
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Subias, Jose Daniel; Daniel-Soriano, Saúl; Gutierrez, Diego; Serrano, Ana; Wang, Beibei; Wilkie, Alexander
Large diffusion models have made a remarkable leap synthesizing high-quality artistic images from text descriptions. However, these powerful pre-trained models still lack control to guide key material appearance properties, such as gloss. In this work, we present a threefold contribution: (1) we analyze how gloss is perceived across different artistic styles (i.e., oil painting, watercolor, ink pen, charcoal, and soft crayon); (2) we leverage our findings to create a dataset with 1,336,272 stylized images of many different geometries in all five styles, including automatically-computed text descriptions of their appearance (e.g., ''A glossy bunny hand painted with an orange soft crayon''); and (3) we train ControlNet to condition Stable Diffusion XL synthesizing novel painterly depictions of new objects, using simple inputs such as edge maps, hand-drawn sketches, or clip arts. Compared to previous approaches, our framework yields more accurate results despite the simplified input, as we show both quantitative and qualitatively.
ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Hong, Seokhyeon; Choi, Soojin; Kim, Chaelin; Cha, Sihun; Noh, Junyong; Bousseau, Adrien; Day, Angela
Despite the growing accessibility of skeletal motion data, integrating it for animating character meshes remains challenging due to diverse configurations of both skeletons and meshes. Specifically, the body scale and bone lengths of the skeleton should be adjusted in accordance with the size and proportions of the mesh, ensuring that all joints are accurately positioned within the character mesh. Furthermore, defining skinning weights is complicated by variations in skeletal configurations, such as the number of joints and their hierarchy, as well as differences in mesh configurations, including their connectivity and shapes. While existing approaches have made efforts to automate this process, they hardly address the variations in both skeletal and mesh configurations. In this paper, we present a novel method for the automatic rigging and skinning of character meshes using skeletal motion data, accommodating arbitrary configurations of both meshes and skeletons. The proposed method predicts the optimal skeleton aligned with the size and proportion of the mesh as well as defines skinning weights for various meshskeleton configurations, without requiring explicit supervision tailored to each of them. By incorporating Diffusion 3D Features (Diff3F) as semantic descriptors of character meshes, our method achieves robust generalization across different configurations. To assess the performance of our method in comparison to existing approaches, we conducted comprehensive evaluations encompassing both quantitative and qualitative analyses, specifically examining the predicted skeletons, skinning weights, and deformation quality.
Atomizer: Beyond Non-Planar Slicing for Fused Filament Fabrication
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Chermain, Xavier; Cocco, Giovanni; Zanni, Cédric; Garner, Eric; Hugron, Pierre-Alexandre; Lefebvre, Sylvain; Attene, Marco; Sellán, Silvia
Fused filament fabrication (FFF) enables users to quickly design and fabricate parts with unprecedented geometric complexity, fine-tuning both the structural and aesthetic properties of each object. Nevertheless, the full potential of this technology has yet to be realized, as current slicing methods fail to fully exploit the deposition freedom offered by modern 3D printers. In this work, we introduce a novel approach to toolpath generation that moves beyond the traditional layer-based concept. We use frames, referred to as atoms, as solid elements instead of slices. We optimize the distribution of atoms within the part volume to ensure even spacing and smooth orientation while accurately capturing the part's geometry. Although these atoms collectively represent the complete object, they do not inherently define a fabrication plan. To address this, we compute an extrusion toolpath as an ordered sequence of atoms that, when followed, provides a collision-free fabrication strategy. This general approach is robust, requires minimal user intervention compared to existing techniques, and integrates many of the best features into a unified framework: precise deposition conforming to non-planar surfaces, effective filling of narrow features - down to a single path - and the capability to locally print vertical structures before transitioning elsewhere. Additionally, it enables entirely new capabilities, such as anisotropic appearance fabrication on curved surfaces.
Augury and Forerunner: Real-Time Feedback Via Predictive Numerical Optimization and Input Prediction
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Graus, J.; Gingold, Y.; Wimmer, Michael; Alliez, Pierre; Westermann, Rüdiger
In many interactive systems, user input initializes and launches an iterative optimization procedure. The goal is to provide assistive feedback to some creation/editing process. Examples include constraint-based GUI layout and complex snapping scenarios. Many geometric problems, such as fitting a shape to data, involve optimizations which may take seconds to complete (or even longer), yet require human guidance. In order to make these optimization routines practical in interactive sessions, simplifications or sacrifices must be made. Canonically, non-convex optimization problems are solved iteratively by taking a series of steps towards a solution. By their nature, there are many locally optimal solutions; which solution is found is highly dependent on an initial guess. There is a fundamental conflict between optimization and interactivity. Interrupting and restarting the optimization every time the user, e.g. moves the mouse prevents any solution from being computed until the user ceases interaction. Continuing to run the optimization procedure computes a perpetually outdated solution. This presents a particular unsolved challenge with respect to direct manipulation. Every time the user, e.g. moves the mouse, the entire optimization must be re-started with the new user input, since returning a stale result associated with the previous user state is undesirable. We propose predictive short-circuiting to reduce this fundamental tension. Our approach memoizes paths in the optimization's configuration space and predicts the trajectory of future optimization in real time, leveraging common C1 continuity assumptions. This enables direct manipulation of formerly sluggish interactions. We demonstrate our approach on geometric fitting tasks. Additionally, we evaluate complementary mouse motion prediction algorithms as a means to discard or skip optimization problems that are irrelevant to the user's intended initial configuration for a targeted optimization procedure. Predicting where the mouse cursor will be located at the end of an operation, such as dragging a model of an engine component into scanned point cloud data to perform geometric alignment, allows us to pre-emptively begin solving the targeted problem before the user finishes their movement. We take advantage of the fact that the prediction indicates the approximate energy basin the optimization procedure will need to explore.
Automatic Inbetweening for Stroke‐Based Painterly Animation
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Barroso, Nicolas; Fondevilla, Amélie; Vanderhaeghe, David
Painterly 2D animation, like the paint‐on‐glass technique, is a tedious task performed by skilled artists, primarily using traditional manual methods. Although CG tools can simplify the creation process, previous works often focus on temporal coherence, which typically results in the loss of the handmade look and feel. In contrast to cartoon animation, where regions are typically filled with smooth gradients, stroke‐based stylized 2D animation requires careful consideration of how shapes are filled, as each stroke may be perceived individually. We propose a method to generate intermediate frames using example keyframes and a motion description. This method allows artists to create only one image for every five to 10 output images in the animation, while the automatically generated intermediate frames provide plausible inbetween frames.
Automatic Reconstruction of Woven Cloth from a Single Close-up Image
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Wu, Chenghao; Khattar, Apoorv; Zhu, Junqiu; Pettifer, Steve; Yan, Lingqi; Montazeri, Zahra; Christie, Marc; Pietroni, Nico; Wang, Yu-Shuen
Digital replication of woven fabrics presents significant challenges across a variety of sectors, from online retail to entertainment industries. To address this, we introduce an inverse rendering pipeline designed to estimate pattern, geometry, and appearance parameters of woven fabrics given a single close-up image as input. Our work is capable of simultaneously optimizing both discrete and continuous parameters without manual interventions. It outputs a wide array of parameters, encompassing discrete elements like weave patterns, ply and fiber number, using Simulated Annealing. It also recovers continuous parameters such as reflection and transmission components, aligning them with the target appearance through differentiable rendering. For irregularities caused by deformation and flyaways, we use 2D Gaussians to approximate them as a post-processing step. Our work does not pursue perfect matching of all fine details, it targets an automatic and end-to-end reconstruction pipeline that is robust to slight camera rotations and room light conditions within an acceptable time (15 minutes on CPU), unlike previous works which are either expensive, require manual intervention, assume given pattern, geometry or appearance, or strictly control camera and light conditions.

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