Feed-forward 3D Gaussian Splatting methods have achieved impressive reconstruction quality for autonomous driving scenes, yet they entangle scene geometry with transient appearance properties such as lighting, weather, and time of day. This coupling prevents relighting, appearance transfer, and consistent rendering across multi-traversal data captured under varying environmental conditions. We present SpectralSplat, a method that disentangles appearance from geometry within a feed-forward Gaussian Splatting framework. Our key insight is to factor color prediction into an appearance-agnostic base stream and and appearance-conditioned adapted stream, both produced by a shared MLP conditioned on a global appearance embedding derived from DINOv2 features. To enforce disentanglement, we train with paired observations generated by a hybrid relighting pipeline that combines physics-based intrinsic decomposition with diffusion based generative refinement, and supervise with complementary consistency, reconstruction, cross-appearance, and base color losses. We further introduce an appearance-adaptable temporal history that stores appearance-agnostic features, enabling accumulated Gaussians to be re-rendered under arbitrary target appearances. Experiments demonstrate that SpectralSplat preserves the reconstruction quality of the underlying backbone while enabling controllable appearance transfer and temporally consistent relighting across driving sequences.
Figures & tables
Figure 1: Appearance-disentangled Gaussian reconstruction. Given input frames (row 1) relighted under progressively varying conditions (row 2), SpectralSplat produces Gaussians whose base colors remain consistent regardless of the input appearance (row 3), while the adapted colors faithfully capture each target lighting condition (row 4). The rendered outputs (row 5) rendered from our predicted Gaussians with swapped appearance embeddings demonstrate the appearance transfer capability.
Table 1: Method comparison. We compare along three axes: Feed-forward – inference without per-scene optimization; Disentangled – appearance is disentangled from geometry; Novel – can render under unseen target appearances ( e.g . from a reference image). SpectralSplat is the only method satisfying all three.
Figure 2: Training pipeline. Original and augmented images share geometry but produce separate features and appearance embeddings. Four losses enforce disentanglement: Linv (base invariance), Laug (augmented reconstruction), Lswap (cross-appearance), and Lbase (base color alignment).
Figure 3: Relighting pipeline. MVInverse + physics rendering is 3D-consistent but flat; IC-Light alone is photorealistic but inconsistent; our hybrid pipeline achieves both.
Figure 4: Cross-appearance results on Waymo. Rows 1–3: source ground truth, adapted render, and base color. Rows 4–6: same for the augmented condition. Base colors from both are nearly identical, confirming appearance-invariance. Rows 7–8: swapping the appearance embedding transfers appearance while preserving geometry.
Waymo
nuScenes
Recon.
NVS
Recon. + NVS
Method
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PixelSplat [ 4 ]
—
—
—
—
—
—
21.51
0.616
0.372
MVSplat [ 5 ]
24.94
0.80
0.23
22.04
0.68
0.34
21.61
0.658
0.295
DepthSplat [ 43 ]
25.38
0.76
0.26
23.86
0.70
0.31
—
—
—
Omni-Scene [ 41 ]
—
—
—
—
—
—
24.27
0.736
0.237
Table 2: Reconstruction quality. SpectralSplat preserves reconstruction quality on both datasets while enabling appearance control. Baseline metrics reported from [ 28 ] . Best in bold , second best underlined .
Figure 5: t-SNE of appearance embeddings. Embeddings cluster by illumination type, confirming the encoder captures meaningful appearance information.
Figure 6: WildGaussians vs. SpectralSplat. Both methods are trained on augmented images and evaluated on the original source images. WildGaussians exhibits artifacts in the reconstruction, while SpectralSplat produces cleaner geometry and appearance.
Method
PSNR ↑
SSIM ↑
LPIPS ↓
Time (per 10 frames)
WildGaussians [ 18 ]
30.42
0.843
0.296
∼ 31 min
SpectralSplat (ours)
27.52
0.862
0.199
∼ 18 s
Table 4: Comparison with WildGaussians. SpectralSplat achieves competitive appearance control in a feed-forward pass, ∼ 100 × faster than per-scene optimization.
PSNR ↑
SSIM ↑
LPIPS ↓
SpectralSplat (full)
Src geom. → Aug app.
21.84
0.678
0.502
Aug geom. → Src app.
19.16
0.579
0.514
w/o Lswap
Src geom. → Aug app.
14.58
0.525
0.525
Aug geom. → Src app.
14.28
0.493
0.534
w/o Lbase
Src geom. → Aug app.
21.32
0.669
0.514
Aug geom. → Src app.
19.03
0.572
0.524
Table 5: Ablation study on Waymo (cross-appearance). We report the swapped configurations (geometry from one source, appearance embedding from the other) to isolate each loss’s contribution to disentanglement. Best in bold .
Figure 7: Appearance transfer grid. Each row renders the same scene geometry under different appearance embeddings. Column 1 : original render. Column 2 : base color ( a=0 ). Columns 3–7 : renders using the appearance embedding from the reference image shown in the top row. The appearance embedding transfers the global color tone and lighting mood while preserving scene geometry.
Figure 8: Cross-appearance results on Waymo 2. Rows 1–3: source ground truth, adapted render, and base color. Rows 4–6: same for the augmented condition. Base colors from both are nearly identical, confirming appearance-invariance. Rows 7–8: swapping the appearance embedding transfers appearance while preserving geometry.
Figure 9: Cross-appearance results on Waymo 3. Rows 1–3: source ground truth, adapted render, and base color. Rows 4–6: same for the augmented condition. Base colors from both are nearly identical, confirming appearance-invariance. Rows 7–8: swapping the appearance embedding transfers appearance while preserving geometry.