EG 2023 - Short Papers

Permanent URI for this collection

https://diglib7.eg.org/handle/10.2312/2633300

Table of Contents

Procedural Modeling and Reconstruction

Photogrammetric Reconstruction of a Stolen Statue

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Zishun Liu, Eugeni L. Doubrovski, Jo M. P. Geraedts, Wenting Wang, Yeung Yam, and Charlie C. L. Wang

Quick-Pro-Build: A Web-based Approach for Quick Procedural 3D Reconstructions of Buildings

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Bela Bohlender, Max Mühlhäuser, and Alejandro Sanchez Guinea

Towards L-System Captioning for Tree Reconstruction

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Jannes S. Magnusson, Anna Hilsmann, and Peter Eisert

Rendering and Simulation

Efficient Needle Insertion Simulation using Hybrid Constraint Solver and Isolated DOFs

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Claire Martin, Ziqiu Zeng, and Hadrien Courtecuisse

Guiding Light Trees for Many-Light Direct Illumination

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Eric Hamann, Alisa Jung, and Carsten Dachsbacher

Out-of-the-loop Autotuning of Metropolis Light Transport with Reciprocal Probability Binning

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Killian Herveau, Hisanari Otsu, and Carsten Dachsbacher

Stylization and Point Clouds

CLIP-based Neural Neighbor Style Transfer for 3D Assets

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Shailesh Mishra and Jonathan Granskog

Text2PointCloud: Text-Driven Stylization for Sparse PointCloud

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Inwoo Hwang, Hyeonwoo Kim, Donggeun Lim, Inbum Park, and Young Min Kim

PointCloudSlicer: Gesture-based Segmentation of Point Clouds

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Hari Hara Gowtham, Amal Dev Parakkat, and Marie-Paule Cani

Perception for Sketches, VR, and Vision

Is Drawing Order Important?

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Sherry Qiu, Zeyu Wang, Leonard McMillan, Holly Rushmeier, and Julie Dorsey

Velocity-Based LOD Reduction in Virtual Reality: A Psychophysical Approach

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David Petrescu, Paul A. Warren, Zahra Montazeri, and Steve Pettifer

Luminance-Preserving and Temporally Stable Daltonization

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Pontus Ebelin, Cyril Crassin, Gyorgy Denes, Magnus Oskarsson, Kalle Åström, and Tomas Akenine-Möller

Subdivision and SDFs

Parallel Loop Subdivision with Sparse Adjacency Matrix

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Kechun Wang and Renjie Chen

Tight Bounding Boxes for Voxels and Bricks in a Signed Distance Field Ray Tracer

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Herman Hansson-Söderlund and Tomas Akenine-Möller

Automatic Step Size Relaxation in Sphere Tracing

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Róbert Bán and Gábor Valasek

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Now showing 1 - 16 of 16

Photogrammetric Reconstruction of a Stolen Statue
(The Eurographics Association, 2023) Liu, Zishun; Doubrovski, Eugeni L.; Geraedts, Jo M. P.; Wang, Wenting; Yam, Yeung; Wang, Charlie C. L.; Babaei, Vahid; Skouras, Melina
In this paper, we propose a method to reconstruct a digital 3D model of a stolen/damaged statue using photogrammetric methods. This task is challenging because the number of available photos for a stolen statue is in general very limited - especially the side/back view photos. Besides using standard structure-from-motion and multi-view stereo methods, we match image pairs with low overlap using sliding windows and maximize the normalized cross-correlation (NCC) based patch-consistency so that the image pairs can be well aligned into a complete model to build the 3D mesh surface. Our method is based on the prior of the planar side on the statue's pedestal, which can cover a large range of statues. We hope this work will motivate more research efforts for the reconstruction of those stolen/damaged statues and heritage preservation.
Quick-Pro-Build: A Web-based Approach for Quick Procedural 3D Reconstructions of Buildings
(The Eurographics Association, 2023) Bohlender, Bela; Mühlhäuser, Max; Guinea, Alejandro Sanchez; Babaei, Vahid; Skouras, Melina
We present Quick-Pro-Build, a web-based approach for quick procedural 3D reconstruction of buildings. Our approach allows users to quickly and easily create realistic 3D models using two integrated reference views: street view and satellite view. We introduce a novel conditional and stochastic shape grammar to represent the procedural models based on the well-established CGA shape grammar. Based on our grammar and user interface, we propose 3 modalities for procedural modeling: 1) model from scratch, 2) copy, paste, and adapt, and 3) summarize, select and adapt. The third modality enables users to model a building by summarizing similar models into an architectural style description, selecting a model from the style description, and adapting it to the target building. Summarizing and selecting allows the third modality to be the most efficient option when modeling a building with a style similar to existing buildings. The third modality is enabled by a novel algorithm that can find and combine similarities from procedural models into a style description and allows learning the preference of the users for one model inside the style description.
Efficient Needle Insertion Simulation using Hybrid Constraint Solver and Isolated DOFs
(The Eurographics Association, 2023) Martin, Claire; Zeng, Ziqiu; Courtecuisse, Hadrien; Babaei, Vahid; Skouras, Melina
This paper introduces a real-time compatible method to improve the location of constraints between a needle and tissues in the context of needle insertion simulation. This method is based on intersections between the Finite Element (FE) meshes of the needle and the tissues. It is coupled with the method of isolating mechanical DOFs and a hybrid solver (implying both direct and iterative resolutions) to respectively generate and solve the constraint problem while reducing the computation time.
Towards L-System Captioning for Tree Reconstruction
(The Eurographics Association, 2023) Magnusson, Jannes S.; Hilsmann, Anna; Eisert, Peter; Babaei, Vahid; Skouras, Melina
This work proposes a novel concept for tree and plant reconstruction by directly inferring a Lindenmayer-System (L-System) word representation from image data in an image captioning approach. We train a model end-to-end which is able to translate given images into L-System words as a description of the displayed tree. To prove this concept, we demonstrate the applicability on 2D tree topologies. Transferred to real image data, this novel idea could lead to more efficient, accurate and semantically meaningful tree and plant reconstruction without using error-prone point cloud extraction, and other processes usually utilized in tree reconstruction. Furthermore, this approach bypasses the need for a predefined L-System grammar and enables species-specific L-System inference without biological knowledge.
Guiding Light Trees for Many-Light Direct Illumination
(The Eurographics Association, 2023) Hamann, Eric; Jung, Alisa; Dachsbacher, Carsten; Babaei, Vahid; Skouras, Melina
Path guiding techniques reduce the variance in path tracing by reusing knowledge from previous samples to build adaptive sampling distributions. The Practical Path Guiding (PPG) approach stores and iteratively refines an approximation of the incident radiance field in a spatio-directional data structure that allows sampling the incident radiance. However, due to the limited resolution in both spatial and directional dimensions, this discrete approximation is not able to accurately capture a large number of very small lights. We present an emitter sampling technique to guide next event estimation (NEE) with a global light tree and adaptive tree cuts that integrates into the PPG framework. In scenes with many lights our technique significantly reduces the RMSE compared to PPG with uniform NEE, while adding close to no overhead in scenes with few light sources. The results show that our technique can also aid the incident radiance learning of PPG in scenes with difficult visibility.
Out-of-the-loop Autotuning of Metropolis Light Transport with Reciprocal Probability Binning
(The Eurographics Association, 2023) Herveau, Killian; Otsu, Hisanari; Dachsbacher, Carsten; Babaei, Vahid; Skouras, Melina
The performance of Markov Chain Monte Carlo (MCMC) rendering methods depends heavily on the mutation strategies and their parameters. We treat the underlying mutation strategies as black-boxes and focus on their parameters. This avoids the need for tedious manual parameter tuning and enables automatic adaptation to the actual scene. We propose a framework for out-of-the-loop autotuning of these parameters. As a pilot example, we demonstrate our tuning strategy for small-step mutations in Primary Sample Space Metropolis Light Transport. Our σ-binning strategy introduces a set of mutation parameters chosen by a heuristic: the inverse probability of the local direction sampling, which captures some characteristics of the local sampling. We show that our approach can successfully control the parameters and achieve better performance compared to non-adaptive mutation strategies.
CLIP-based Neural Neighbor Style Transfer for 3D Assets
(The Eurographics Association, 2023) Mishra, Shailesh; Granskog, Jonathan; Babaei, Vahid; Skouras, Melina
We present a method for transferring the style from a set of images to the texture of a 3D object. The texture of an asset is optimized with a differentiable renderer and losses using pretrained deep neural networks. More specifically, we utilize a nearest-neighbor feature matching (NNFM) loss with CLIP-ResNet50 that we extend to support multiple style images. We improve color accuracy and artistic control with an extra loss on user-provided or automatically extracted color palettes. Finally, we show that a CLIP-based NNFM loss provides a different appearance over a VGG-based one by focusing more on textural details over geometric shapes. However, we note that user preference is still subjective.
PointCloudSlicer: Gesture-based Segmentation of Point Clouds
(The Eurographics Association, 2023) Gowtham, Hari Hara; Parakkat, Amal Dev; Cani, Marie-Paule; Babaei, Vahid; Skouras, Melina
Segmentation is a fundamental problem in point-cloud processing, addressing points classification into consistent regions, the criteria for consistency being based on the application. In this paper, we introduce a simple, interactive framework enabling the user to quickly segment a point cloud in a few cutting gestures in a perceptually consistent way. As the user perceives the limit of a shape part, they draw a simple separation stroke over the current 2D view. The point cloud is then segmented without needing any intermediate meshing step. Technically, we find an optimal, perceptually consistent cutting plane constrained by user stroke and use it for segmentation while automatically restricting the extent of the cut to the closest shape part from the current viewpoint. This enables users to effortlessly segment complex point clouds from an arbitrary viewpoint with the possibility of handling self-occlusions.
Text2PointCloud: Text-Driven Stylization for Sparse PointCloud
(The Eurographics Association, 2023) Hwang, Inwoo; Kim, Hyeonwoo; Lim, Donggeun; Park, Inbum; Kim, Young Min; Babaei, Vahid; Skouras, Melina
We present Text2PointCloud, a method to process sparse, noisy point cloud input and generate high-quality stylized output. Given point cloud data, our iterative pipeline stylizes and deforms points guided by a text description and gradually densifies the point cloud. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated 3D datasets with high-quality textures, which are produced by skillful artists or high-resolution colored 3D models. Also, since we represent 3D shapes as a point cloud, we can visualize fine-grained geometric variations with a complex topology such as flowers or fire. To the best of our knowledge, it is the first approach for directly stylizing the uncolored, sparse point cloud input without converting it into a mesh or implicit representation, which might fail to express the original information in the measurements, especially when the object exhibits complex topology.
Is Drawing Order Important?
(The Eurographics Association, 2023) Qiu, Sherry; Wang, Zeyu; McMillan, Leonard; Rushmeier, Holly; Dorsey, Julie; Babaei, Vahid; Skouras, Melina
The drawing process is crucial to understanding the final result of a drawing. There has been a long history of understanding human drawing; what kinds of strokes people use and where they are placed. An area of interest in Artificial Intelligence is developing systems that simulate human behavior in drawing. However, there has been little work done to understand the order of strokes in the drawing process. Without sufficient understanding of natural drawing order, it is difficult to build models that can generate natural drawing processes. In this paper, we present a study comparing multiple types of stroke orders to confirm findings from previous work and demonstrate that multiple orderings of the same set of strokes can be perceived as human-drawn and different stroke order types achieve different perceived naturalness depending on the type of image prompt.
Velocity-Based LOD Reduction in Virtual Reality: A Psychophysical Approach
(The Eurographics Association, 2023) Petrescu, David; Warren, Paul A.; Montazeri, Zahra; Pettifer, Steve; Babaei, Vahid; Skouras, Melina
Virtual Reality headsets enable users to explore the environment by performing self-induced movements. The retinal velocity produced by such motion reduces the visual system's ability to resolve fine detail. We measured the impact of self-induced head rotations on the ability to detect quality changes of a realistic 3D model in an immersive virtual reality environment. We varied the Level of Detail (LOD) as a function of rotational head velocity with different degrees of severity. Using a psychophysical method, we asked 17 participants to identify which of the two presented intervals contained the higher quality model under two different maximum velocity conditions. After fitting psychometric functions to data relating the percentage of correct responses to the aggressiveness of LOD manipulations, we identified the threshold severity for which participants could reliably (75%) detect the lower LOD model. Participants accepted an approximately four-fold LOD reduction even in the low maximum velocity condition without a significant impact on perceived quality, suggesting that there is considerable potential for optimisation when users are moving (increased range of perceptual uncertainty). Moreover, LOD could be degraded significantly more (around 84%) in the maximum head velocity condition, suggesting these effects are indeed speed-dependent.
Luminance-Preserving and Temporally Stable Daltonization
(The Eurographics Association, 2023) Ebelin, Pontus; Crassin, Cyril; Denes, Gyorgy; Oskarsson, Magnus; Åström, Kalle; Akenine-Möller, Tomas; Babaei, Vahid; Skouras, Melina
We propose a novel, real-time algorithm for recoloring images to improve the experience for a color vision deficient observer. The output is temporally stable and preserves luminance, the most important visual cue. It runs in 0.2 ms per frame on a GPU.
Tight Bounding Boxes for Voxels and Bricks in a Signed Distance Field Ray Tracer
(The Eurographics Association, 2023) Hansson-Söderlund, Herman; Akenine-Möller, Tomas; Babaei, Vahid; Skouras, Melina
We present simple methods to compute tight axis-aligned bounding boxes for voxels and for bricks of voxels in a signed distance function renderer based on ray tracing. Our results show total frame time reductions of 20-31% in a real-time path tracer.
Parallel Loop Subdivision with Sparse Adjacency Matrix
(The Eurographics Association, 2023) Wang, Kechun; Chen, Renjie; Babaei, Vahid; Skouras, Melina
Subdivision surface is a popular technique for geometric modeling. Recently, several parallel implementations have been developed for Loop subdivision on the GPU. However, these methods are built on complex data structures which complicate the implementation and affect the performance, especially on the GPU. In this work, we propose to simply use the sparse adjacency matrix which enables us to implement the Loop subdivision scheme in the most straightforward manner. Our implementation run entirely on the GPU and achieves high performance in runtime with significantly lower memory consumption than the state-of-the-art. Through extensive experiments and comparisons, we demonstrate the efficacy and efficiency of our method.
Automatic Step Size Relaxation in Sphere Tracing
(The Eurographics Association, 2023) Bán, Róbert; Valasek, Gábor; Babaei, Vahid; Skouras, Melina
We propose a robust auto-relaxed sphere tracing method that automatically scales its step sizes based on data from previous iterations. It possesses a scalar hyperparemeter that is used similarly to the learning rate of gradient descent methods. We show empirically that this scalar degree of freedom has a smaller effect on performance than the step-scale hyperparameters of concurrent sphere tracing variants. Additionally, we compare the performance of our algorithm to these both on procedural and discrete signed distance input and show that it outperforms or performs up to par to the most efficient method, depending on the limit on iteration counts. We also verify that our method takes significantly fewer robustness-preserving sphere trace fallback steps, as it generates fewer invalid, over-relaxed step sizes.
EUROGRAPHICS 2023: Short Papers Frontmatter
(Eurographics Association, 2023) Babaei, Vahid; Skouras, Melina; Babaei, Vahid; Skouras, Melina

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