ELM-GS: Environment Lighting Modulation on Gaussian Splatting for Efficient Inverse Rendering

Inverse rendering aims to recover intrinsic scene properties from images, enabling applications such as scene editing. Recent Gaussian Splatting (GS)-based methods achieve high-quality Physically-based Rendering (PBR) but often incur substantial computational costs due to expensive ray tracing. Ambient occlusion (AO)-based alternatives reduce this cost, yet occlusion estimation overhead and albedo–-shading entanglement still limit their efficiency and editability. Inspired by asset-production pipelines that precompute AO as texture maps for efficient lighting modulation, we propose ELM-GS, an efficient 3DGS framework for inverse rendering with environment lighting modulation. ELM-GS introduces a lighting-aware scene representation that decodes learnable features into primitive-wise AO and environment compensation (EC) terms, which respectively attenuate and enhance local environment illumination. Both modulation terms can be splatted into G-buffer textures for efficient PBR. To improve physical plausibility, we derive multi-view occlusion priors via Screen Space Ambient Occlusion to explicitly supervise AO learning. We further construct Mat-Edit, a synthetic benchmark with pixel-level material annotations to support fine-grained material editing, together with ground-truth AO and material-edited images for comprehensively evaluating the performance of inverse rendering. Experiments show that ELM-GS achieves state-of-the-art efficiency in inverse rendering and novel view synthesis while outperforming existing AO-based 3DGS methods in intrinsic decomposition and scene editing. The code will be made publicly available.