Depth Peeling for High-Fidelity Gaussian-Enhanced Surfel Rendering

Keyang Ye, Hongzhi Wu, Kun Zhou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 22561-22570

Novel view synthesis has been significantly advanced by NeRFs and 3D Gaussian Splatting (3DGS), which require ordering volumetric samples or primitives for correct color blending. While the recent Gaussian-Enhanced Surfels (GES) enable high-performance, sort-free rendering, they suffer from aliasing artifacts and suboptimal reconstruction. To address these limitations, we propose DP-GES, a novel representation that combines 2D opaque surfels with semi-transparent boundaries to represent coarse-scale geometry and appearance, and 3D Gaussians surrounding the surfels to supplement fine-scale details. We employ Depth Peeling to achieve accurate per-pixel ordering for surfel rendering, which enables sort-free Gaussian splatting with correct transmittance modulation, effectively eliminating aliasing and popping artifacts while facilitating a fully differentiable joint optimization. Extensive experiments demonstrate that our method achieves superior novel view synthesis quality and compares favorably against state-of-the-art techniques across a wide range of scenes.