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D scene structure. The Gaussian weight computation also differs substantially across these approaches. SortFreeGS leverages the Gaussian depth to modulate its contribution but does not account for the Gaussian scale, which we find to be critical. GES, on the other hand, relies on a two-stage rendering. It first renders a depth image using conventional volume rendering and then filters out distant Gaussians by comparing their depths against the rendered depth map for later sorting-free rendering. This two-stage rendering pipeline relies on precise depth rendering and increases computational load, so it is not well-suited for mobile deployment. In contrast, Mobile-GS exploits both depth and scale attributes of each Gaussian to compute an importance weight, reflecting the intuition that farther Gaussians should have lower contribution, while larger Gaussians typically provide more meaningful rendering evidence. Theoretically, A key challenge for sorting-free methods is the potential order ambiguity in regions where geometry overlaps. SortFreeGS attempts to address this by introducing additional spherical harmonics parameters to model view-dependent opacity. However, this design incurs significant overhead and is unfavorable for practical mobile usage. Our Mobile-GS resolves this limitation by enhancing the view-dependent effect through a learnable parameter ϕ\phi, predicted by a lightweight MLP conditioned on Gaussian attributes. This formulation achieves high-quality rendering without introducing a prohibitive computational or memory burden. Overall, Mobile-GS is carefully tailored to minimize resource consumption, reduce Gaussian parameter storage, and maintain real-time rendering performance on mobile hardware. Table 8: Comparison with different sorting-free methods. SortFreeGS* means its quantized version. We report metrics on the Mip-NeRF 360 dataset for the mobile equipped with the Snapdragon 8 Gen 3 GPU. FPS* means the rendering speed on the mobile. Method Rendering Weighting PSNR ↑\uparrow Storage ↓\downarrow FPS*↑\uparrow SortFreeGS* 𝐂=cbgwbg+∑i=1𝒩ciαiw(di)wbg+∑i=1𝒩αiw(di)\mathbf{C}=\frac{c_{bg}w_{bg}+\sum_{i=1}^{\mathcal{N}}c_{i}\alpha_{i}w(d_{i})}{w_{bg}+\sum_{i=1}^{\mathcal{N}}\alpha_{i}w(d_{i})} w(di)=exp(−σdiβ)w(d_{i})=\exp\!\left(-\sigma d_{i}^{\beta}\right) 26.74 64.3 MB 18 GES 𝐂=CsWs+CGWs+WG\mathbf{C}=\frac{C_{s}W_{s}+C_{G}}{W_{s}+W_{G}} WG(𝐱^)=∑i=
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Achieve real-time, high-quality 3D Gaussian Splatting on mobile devices by eliminating depth sorting and compressing the Gaussian representation.
By explicitly encoding 3D geometry, GeoDrive achieves more realistic and controllable autonomous driving scene modeling, outperforming prior world models in action accuracy and spatial awareness.
