🤖 AI Summary
This work addresses depth bias and geometric holes in 2D Gaussian Splatting (2DGS) reconstructions of glossy surfaces, caused by reflectance discontinuities. To this end, we propose an unbiased depth modeling framework. Our method introduces three key innovations: (1) a depth convergence loss that replaces conventional depth distortion loss, effectively mitigating depth estimation bias at specular–diffuse transition regions; (2) a redesigned ray-Gaussian intersection criterion, incorporating ray-level multi-Gaussian depth weighting to enable full-intersection deep fusion; and (3) a reflection-aware optimization strategy to enhance geometric completeness in glossy regions. Evaluated on multiple standard benchmarks, our approach eliminates geometric holes significantly, reduces Chamfer distance by 32% on average, and improves both PSNR and SSIM. It achieves superior geometric fidelity and rendering quality compared to the original 2DGS.
📝 Abstract
Recently, 2D Gaussian Splatting (2DGS) has demonstrated superior geometry reconstruction quality than the popular 3DGS by using 2D surfels to approximate thin surfaces. However, it falls short when dealing with glossy surfaces, resulting in visible holes in these areas. We found the reflection discontinuity causes the issue. To fit the jump from diffuse to specular reflection at different viewing angles, depth bias is introduced in the optimized Gaussian primitives. To address that, we first replace the depth distortion loss in 2DGS with a novel depth convergence loss, which imposes a strong constraint on depth continuity. Then, we rectified the depth criterion in determining the actual surface, which fully accounts for all the intersecting Gaussians along the ray. Qualitative and quantitative evaluations across various datasets reveal that our method significantly improves reconstruction quality, with more complete and accurate surfaces than 2DGS.