MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections
- URL: http://arxiv.org/abs/2405.11921v1
- Date: Mon, 20 May 2024 09:58:03 GMT
- Title: MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections
- Authors: Jiayue Liu, Xiao Tang, Freeman Cheng, Roy Yang, Zhihao Li, Jianzhuang Liu, Yi Huang, Jiaqi Lin, Shiyong Liu, Xiaofei Wu, Songcen Xu, Chun Yuan,
- Abstract summary: MirrorGaussian is the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting.
We introduce an intuitive dual-rendering strategy that enables differentiableization of both the real-world 3D Gaussians and the mirrored counterpart.
Our approach significantly outperforms existing methods, achieving state-of-the-art results.
- Score: 58.003014868772254
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results. Project page: https://mirror-gaussian.github.io/.
Related papers
- EnvGS: Modeling View-Dependent Appearance with Environment Gaussian [78.74634059559891]
EnvGS is a novel approach that employs a set of Gaussian primitives as an explicit 3D representation for capturing reflections of environments.
To efficiently render these environment Gaussian primitives, we developed a ray-tracing-based reflection that leverages the GPU's RT core for fast rendering.
Results from multiple real-world and synthetic datasets demonstrate that our method produces significantly more detailed reflections.
arXiv Detail & Related papers (2024-12-19T18:59:57Z) - Gaussian Splatting in Mirrors: Reflection-Aware Rendering via Virtual Camera Optimization [14.324573496923792]
3D-GS often misinterprets reflections as virtual spaces, resulting in blurred and inconsistent multi-view rendering within mirrors.
Our paper presents a novel method aimed at obtaining high-quality multi-view consistent reflection rendering by modelling reflections as physically-based virtual cameras.
arXiv Detail & Related papers (2024-10-02T14:53:24Z) - Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections [26.02117310176884]
We tackle the problem of generating highly realistic and plausible mirror reflections using diffusion-based generative models.
We propose a novel depth-conditioned inpainting method called MirrorFusion, which generates high-quality, realistic, shape and appearance-aware reflections of real-world objects.
MirrorFusion outperforms state-of-the-art methods on SynMirror, as demonstrated by extensive quantitative and qualitative analysis.
arXiv Detail & Related papers (2024-09-23T02:59:07Z) - Mirror-3DGS: Incorporating Mirror Reflections into 3D Gaussian Splatting [27.361324194709155]
Mirror-3DGS is a novel framework designed to accurately handle mirror geometries and reflections.
By incorporating mirror attributes into 3DGS, Mirror-3DGS simulates a mirrored viewpoint from behind the mirror, enhancing the realism of scene renderings.
arXiv Detail & Related papers (2024-04-01T15:16:33Z) - 2D Gaussian Splatting for Geometrically Accurate Radiance Fields [50.056790168812114]
3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed without baking.
We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images.
We demonstrate that our differentiable terms allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering.
arXiv Detail & Related papers (2024-03-26T17:21:24Z) - GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization [62.13932669494098]
This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to factorize the scene into material properties, light, and geometry.
We compute the normal of each 3D Gaussian using the shortest eigenvector, with a directional masking scheme forcing accurate normal estimation without external supervision.
We adopt an efficient voxel-based indirect illumination tracing scheme that stores direction-aware outgoing radiance in each 3D Gaussian to disentangle secondary illumination for approximating multi-bounce light transport.
arXiv Detail & Related papers (2023-12-08T16:05:15Z) - Mirror-Aware Neural Humans [21.0548144424571]
We develop a consumer-level 3D motion capture system that starts from off-the-shelf 2D poses by automatically calibrating the camera.
We empirically demonstrate the benefit of learning a body model and accounting for occlusion in challenging mirror scenes.
arXiv Detail & Related papers (2023-09-09T10:43:45Z) - Mirror-NeRF: Learning Neural Radiance Fields for Mirrors with
Whitted-Style Ray Tracing [33.852910220413655]
We present a novel neural rendering framework, named Mirror-NeRF, which is able to learn accurate geometry and reflection of the mirror.
Mirror-NeRF supports various scene manipulation applications with mirrors, such as adding new objects or mirrors into the scene and synthesizing the reflections of these new objects in mirrors.
arXiv Detail & Related papers (2023-08-07T03:48:07Z) - Symmetry-Aware Transformer-based Mirror Detection [85.47570468668955]
We propose a dual-path Symmetry-Aware Transformer-based mirror detection Network (SATNet)
SATNet includes two novel modules: Symmetry-Aware Attention Module (SAAM) and Contrast and Fusion Decoder Module (CFDM)
Experimental results show that SATNet outperforms both RGB and RGB-D mirror detection methods on all available mirror detection datasets.
arXiv Detail & Related papers (2022-07-13T16:40:01Z) - Inverting Generative Adversarial Renderer for Face Reconstruction [58.45125455811038]
In this work, we introduce a novel Generative Adversa Renderer (GAR)
GAR learns to model the complicated real-world image, instead of relying on the graphics rules, it is capable of producing realistic images.
Our method achieves state-of-the-art performances on multiple face reconstruction.
arXiv Detail & Related papers (2021-05-06T04:16:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.