Organizations: University of Tokyo, Japan. · Tohoku University, Japan. · University of California at Merced and Google DeepMind, US. · University of Tokyo and RIKEN AIP, Japan.
High-quality image acquisition in real-world environments remains challenging due to complex illumination variations and inherent limitations of camera imaging pipelines. These issues are exacerbated in multi-view capture, where differences in lighting, sensor responses, and image signal processor (ISP) configurations introduce photometric and chromatic inconsistencies that violate the assumptions of photometric consistency underlying modern 3D novel view synthesis (NVS) methods, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), leading to degraded reconstruction and rendering quality. We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions. Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction. We further design unsupervised objectives that jointly enforce lightness correction and multi-view geometric and photometric consistency. Extensive experiments demonstrate state-of-the-art performance across challenging scenarios, including low-light, overexposure, and complex luminance and chromatic variations. Unlike prior approaches that modify the underlying representation, our method preserves the explicit 3DGS formulation, improving reconstruction fidelity while maintaining real-time rendering efficiency.
Figures & tables
Fig. 1: (a) An overview of Luminance-GS++ , designed to address multi-view images affected by real-world illumination and color degradations via joint global and local adjustments, enabling robust 3DGS reconstruction. (b) Performance comparison between Luminance-GS++ and state-of-the-art methods [ 36 , 28 , 34 , 37 ] under diverse illumination and color conditions.
Fig. 2: Progression from 3DGS [ 6 ] to Luminance-GS [ 36 ] and Luminance-GS++, highlighting joint optimization of input images and 3DGS attributes through global adjustment and local refinement. Multi-view rendering examples illustrate the resulting improvements.
Fig. 3: A low-light “chair” scene from the LOM dataset [ 28 ] . Our global adjustment enhances overall illumination, while our local refinement improves fine details and the quality of pseudo label Cout .
Fig. 4: An overview of the Luminance-GS++ pipeline . Up : Joint optimization of 3D Gaussians with dual color attributes ci and ciout to render input images Cin and pseudo-enhanced outputs Cout . Down : Pseudo-enhancement is achieved through global adjustment (view-adaptive color matrix mapping and curve adjustment; stages (I)–(III)) followed by local refinement using a pixel-wise residual map.
Fig. 5: An illustration of our spatial consistency loss function Lspa , and comparisons with the spatial consistency loss in Aleth-NeRF [ 28 ] .
Fig. 6: (a) Per-view color matrix mapping with learnable transformation Mk . (b) Architecture of the view-adaptive curve generator, which predicts curve bias Lkb from input image Ckin and camera pose. (c) Structure of the view-adaptive parameter generator.
Fig. 7: Effect of varying color temperatures (cool, warm, and mixed) on NVS. We compare the results with and without the color correction loss Lcc and visualize the corresponding color distributions of rendered novel views using scatter plots.
Fig. 8: Chromaticity consistency visualization in CIELAB color space. Pixel chromaticity coordinates ( a∗,b∗ ) from rendered views are plotted for a static region. Luminance-GS++ produces a compact and concentrated distribution, indicating improved cross-view color consistency and stable photometric appearance.
Fig. 9: Efficiency comparison: FPS and peak GPU memory (GB) on the LOM [ 28 ] and Mip-NeRF 360 [ 5 ] datasets. Methods closer to the upper-left corner indicate better efficiency.
Fig. 10: Experimental results on the low-light and overexposure subsets of LOM dataset [ 28 ] . The first column shows the results of the basic 3DGS [ 6 ] , while the second and third columns present the results of various comparison methods [ 28 , 29 , 74 , 20 , 19 , 14 ] . The last two columns show the results of our Luminance-GS [ 36 ] and Luminance-GS++. Some regions are zoomed in to better observe the details.
Fig. 11: Qualitative comparisons of NVS results under lightness, color, and mixed degradation. The last column presents our Luminance-GS++, while the remaining columns correspond to competing methods [ 6 , 34 , 31 , 36 , 37 ] . Zoomed-in regions are provided to highlight details.
Fig. 12: Visualization of the effectiveness of different loss components.