EG 2025 - Full Papers - CGF 44-Issue 2

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https://diglib.eg.org/handle/10.2312/3607136

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Browsing EG 2025 - Full Papers - CGF 44-Issue 2 by Subject "based models"

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    D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Kappel, Moritz; Hahlbohm, Florian; Scholz, Timon; Castillo, Susana; Theobalt, Christian; Eisemann, Martin; Golyanik, Vladislav; Magnor, Marcus; Bousseau, Adrien; Day, Angela
    Dynamic reconstruction and spatiotemporal novel-view synthesis of non-rigidly deforming scenes recently gained increased attention. While existing work achieves impressive quality and performance on multi-view or teleporting camera setups, most methods fail to efficiently and faithfully recover motion and appearance from casual monocular captures. This paper contributes to the field by introducing a new method for dynamic novel view synthesis from monocular video, such as casual smartphone captures. Our approach represents the scene as a dynamic neural point cloud, an implicit time-conditioned point distribution that encodes local geometry and appearance in separate hash-encoded neural feature grids for static and dynamic regions. By sampling a discrete point cloud from our model, we can efficiently render high-quality novel views using a fast differentiable rasterizer and neural rendering network. Similar to recent work, we leverage advances in neural scene analysis by incorporating data-driven priors like monocular depth estimation and object segmentation to resolve motion and depth ambiguities originating from the monocular captures. In addition to guiding the optimization process, we show that these priors can be exploited to explicitly initialize our scene representation to drastically improve optimization speed and final image quality. As evidenced by our experimental evaluation, our dynamic point cloud model not only enables fast optimization and real-time frame rates for interactive applications, but also achieves competitive image quality on monocular benchmark sequences. Our code and data are available online https://moritzkappel.github.io/projects/dnpc/.
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    Efficient Perspective-Correct 3D Gaussian Splatting Using Hybrid Transparency
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Hahlbohm, Florian; Friederichs, Fabian; Weyrich, Tim; Franke, Linus; Kappel, Moritz; Castillo, Susana; Stamminger, Marc; Eisemann, Martin; Magnor, Marcus; Bousseau, Adrien; Day, Angela
    3D Gaussian Splats (3DGS) have proven a versatile rendering primitive, both for inverse rendering as well as real-time exploration of scenes. In these applications, coherence across camera frames and multiple views is crucial, be it for robust convergence of a scene reconstruction or for artifact-free fly-throughs. Recent work started mitigating artifacts that break multi-view coherence, including popping artifacts due to inconsistent transparency sorting and perspective-correct outlines of (2D) splats. At the same time, real-time requirements forced such implementations to accept compromises in how transparency of large assemblies of 3D Gaussians is resolved, in turn breaking coherence in other ways. In our work, we aim at achieving maximum coherence, by rendering fully perspective-correct 3D Gaussians while using a high-quality approximation of accurate blending, hybrid transparency, on a per-pixel level, in order to retain real-time frame rates. Our fast and perspectively accurate approach for evaluation of 3D Gaussians does not require matrix inversions, thereby ensuring numerical stability and eliminating the need for special handling of degenerate splats, and the hybrid transparency formulation for blending maintains similar quality as fully resolved per-pixel transparencies at a fraction of the rendering costs. We further show that each of these two components can be independently integrated into Gaussian splatting systems. In combination, they achieve up to 2× higher frame rates, 2× faster optimization, and equal or better image quality with fewer rendering artifacts compared to traditional 3DGS on common benchmarks.
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    Learning Fast 3D Gaussian Splatting Rendering using Continuous Level of Detail
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Milef, Nicholas; Seyb, Dario; Keeler, Todd; Nguyen-Phuoc, Thu; Bozic, Aljaz; Kondguli, Sushant; Marshall, Carl; Bousseau, Adrien; Day, Angela
    3D Gaussian splatting (3DGS) has shown potential for rendering photorealistic 3D scenes in real-time. Unfortunately, rendering these scenes on less powerful hardware is still a challenge, especially with high-resolution displays. We introduce a continuous level of detail (CLOD) algorithm and demonstrate how our method can improve performance while preserving as much quality as possible. Our approach learns to order splats based on importance and optimize them such that a representative and realistic scene can be rendered for an arbitrary splat count. Our method does not require any additional memory or rendering overhead and works with existing 3DGS renderers. We also demonstrate the flexibility of our CLOD method by extending it with distance-based LOD selection, foveated rendering, and budget-based rendering.
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    Learning Image Fractals Using Chaotic Differentiable Point Splatting
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Djeacoumar, Adarsh; Mujkanovic, Felix; Seidel, Hans-Peter; Leimkühler, Thomas; Bousseau, Adrien; Day, Angela
    Fractal geometry, defined by self-similar patterns across scales, is crucial for understanding natural structures. This work addresses the fractal inverse problem, which involves extracting fractal codes from images to explain these patterns and synthesize them at arbitrary finer scales. We introduce a novel algorithm that optimizes Iterated Function System parameters using a custom fractal generator combined with differentiable point splatting. By integrating both stochastic and gradient-based optimization techniques, our approach effectively navigates the complex energy landscapes typical of fractal inversion, ensuring robust performance and the ability to escape local minima. We demonstrate the method's effectiveness through comparisons with various fractal inversion techniques, highlighting its ability to recover high-quality fractal codes and perform extensive zoom-ins to reveal intricate patterns from just a single image.