InstantSticker: Realistic Decal Blending via Disentangled Object Reconstruction
Not on the map. Why?
Abstract (arXiv)
We present InstantSticker, a disentangled reconstruction pipeline based on Image-Based Lighting (IBL), which focuses on highly realistic decal blending, simulates stickers attached to the reconstructed surface, and allows for instant editing and real-time rendering. To achieve stereoscopic impression of the decal, we introduce shadow factor into IBL, which can be adaptively optimized during training. This allows the shadow brightness of surfaces to be accurately decomposed rather than baked into the diffuse color, ensuring that the edited texture exhibits authentic shading. To address the issues of warping and blurriness in previous methods, we apply As-Rigid-As-Possible (ARAP) parameterization to pre-unfold a specified area of the mesh and use the local UV mapping combined with a neural texture map to enhance the ability to express high-frequency details in that area. For instant editing, we utilize the Disney BRDF model, explicitly defining material colors with 3-channel diffuse albedo. This enables instant replacement of albedo RGB values during the editing process, avoiding the prolonged optimization required in previous approaches. In our experiment, we introduce the Ratio Variance Warping (RVW) metric to evaluate the local geometric warping of the decal area. Extensive experimental results demonstrate that our method surpasses previous decal blending methods in terms of editing quality, editing speed and rendering speed, achieving the state-of-the-art.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 9 Apr 2025.
Builds on
14 of 32 references.
References · 14
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2018On the Spectral Bias of Neural Networkscited by 1,341
- 2020Accelerating 3D Deep Learning with PyTorch3Dcited by 784
- 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 366
- 2021PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relightingcited by 335
- 2022Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstructioncited by 212
- 2023NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Imagescited by 163
- 2021NeuTex: Neural Texture Mapping for Volumetric Neural Renderingcited by 98
- 2022Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstructioncited by 94
- 2022Compressible-composable NeRF via Rank-residual Decompositioncited by 92
- 2023GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorizationcited by 87
- 2023Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflectioncited by 69
2 more
- 2023Nuvo: Neural UV Mapping for Unruly 3D Representationscited by 29
- 2022Boosting Point Clouds Rendering via Radiance Mappingcited by 11
Built on by
Not cited yet.