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EnvGS: Modeling View-Dependent Appearance with Environment Gaussian

CVPR 2025 · 3D vision › Gaussian splatting · approach: Sparse-view Gaussian splatting for novel views · 8 citations (OpenAlex)

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Proposes EnvGS, which models scene reflections with an explicit set of environment Gaussian primitives traced via a custom OptiX ray tracer over 2D Gaussians, blended with base Gaussians for real-time novel view synthesis of reflective scenes.

Problem

Real-time novel view synthesis of real-world scenes with complex high-frequency and near-field specular reflections, which SH-based 3DGS and distant-light environment-map methods struggle to model.

Approach

Replace environment maps with an explicit set of environment Gaussian primitives that are ray-traced along reflection directions using an OptiX-based differentiable 2DGS ray tracer, blended with base 2D Gaussians; joint optimization with monocular normal and perceptual losses.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add environment Gaussian primitives as the reflection representationEnvGS · Ref-RealSSIM: 0.659 -> 0.671Helped
Add environment Gaussian primitives as the reflection representationEnvGS · AudiPSNR: 26.171 -> 28.629Helped
Add environment Gaussian primitives as the reflection representationEnvGS · NeRF-Casting ScenesPSNR: 31.740 -> 30.444Hurt
Add environment Gaussian primitives as the reflection representationEnvGS · NeRF-Casting ScenesLPIPS: 0.105 -> 0.148Hurt
Add joint optimization of base and environment Gaussians (ablation)EnvGS · Ref-RealPSNR: 24.034 -> 24.617Helped
Add monocular normal constraint (ablation)EnvGS · Ref-RealPSNR: 24.107 -> 24.617Helped
Use Gaussian environment representation instead of an environment map (ablation)EnvGS · Ref-RealPSNR: 24.145 -> 24.617Helped
Add color sabotage (ablation)EnvGS · Ref-RealPSNR: 24.268 -> 24.617Helped
Add normal propagation (ablation)EnvGS · Ref-RealPSNR: 24.192 -> 24.617Helped
Add perceptual loss (ablation)EnvGS · Ref-RealPSNR: 24.567 -> 24.617No clear effect

Surprising findings

Limitations

Experimental setup

Datasets: Ref-Real NeRF-Casting Shiny Scenes Shiny Blender Mip-NeRF 360 self-captured real-world scenes

Benchmarks: Ref-Real NeRF-Casting Shiny Scenes Shiny Blender Mip-NeRF 360

Metrics: PSNR SSIM LPIPS FPS training time

Baselines: 3DGS 2DGS GaussianShader 3DGS-DR Ref-NeRF NeRF-Casting Zip-NeRF UniSDF ENVIDR NDE

Builds on: 2D Gaussian Splatting 3D Gaussian Splatting OptiX 3DGS-DR Ref-NeRF

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