RIDE‑Splat is a new pipeline that removes windshield reflections from driving videos to produce clean 3D reconstructions using 3D Gaussian Splatting. It assigns world‑consistent transmission to the 3D scene and models the camera‑attached reflection as a shared signed RGB image, learning geometry and reflection in two passes before fitting a clean 3DGS model. The authors also release the RIDE‑Splat Dataset, the first benchmark for reflection removal in driving scenes, and demonstrate that RIDE‑Splat outperforms existing methods on nine aggregate metrics, improving PSNR by up to 3.59 dB and reducing LPIPS by up to 0.0876.
arXiv:2606. 16278v1 Announce Type: cross Abstract: Long-tail hazardous scenarios are essential for safety-oriented autonomous driving, yet they are difficult to collect and reproduce at scale.
PAM-ToD is a lightweight plug‑in that adapts a pre‑trained 3D Gaussian Splatting road scene to a new time of day by learning color corrections while keeping the original 3DGS parameters fixed. It scales each Gaussian’s existing color to model illumination changes and adds a brightness term for effects such as street lamps, learning these corrections from a few anchor images without separately estimating albedo and illumination. The method includes a spatial regularization to avoid abrupt changes and is evaluated on the CARLA-ToD benchmark, achieving higher PSNR and lower LPIPS than baselines in both static and dynamic settings.
DriveWorld3D is a framework that integrates temporal video learning, multi‑sensor measurement constraints, and offline generative simulation to build a unified 3D world model for autonomous driving. Its online component tracks road surfaces, volumetric occupancy, dynamic entities, and road relations while explicitly managing physical occupancy, observability, and evidence provenance. The offline component reconstructs scenes with neural radiance fields or 3D Gaussian Splatting and expands rare conditions using generative models, but limits generated content from being used as geometric ground truth.
arXiv:2607.05598v2 Announce Type: replace-cross Abstract: Novel View Synthesis (NVS) methods, such as 3D Gaussian Splatting (3DGS), rely on the assumption of clean, multi-view consistent, posed input...
arXiv:2601.00913v2 Announce Type: replace Abstract: A pretrained 3D Gaussian Splatting (3DGS) reconstruction can reproduce its capture views well while retaining off-object structure that becomes vis...
Reflections from shiny objects and glass facades naturally extend the field of view of a camera, capturing the surrounding environment without the need to pan the camera or acquire a full panorama. We propose PanoLess, a Gaussian-splat-based framework that reconstructs the surrounding environment as a distant illumination map from images captured on only one side of a reflective surface.
GS‑Net is a lightweight plug‑and‑play module that expands sparse Structure‑from‑Motion point clouds into dense Gaussian primitives, enabling cross‑sensor view synthesis for autonomous driving. It learns a generalizable initialization for 3D Gaussian Splatting, improving rendering quality for both interpolated and extrapolated camera viewpoints. The authors introduce CARLA‑NVS, a benchmark with 12 uniformly spaced cameras, and show that GS‑Net outperforms standard 3DGS by 2.08 dB PSNR on interpolated views and 1.86 dB on extrapolated views while being 50× faster to initialize.
arXiv:2610.01876v1 Announce Type: new Abstract: A surface photographed under even light presents nearly the same appearance from every angle; the same surface under uneven light does not. Exposure ch...
arXiv:2609.36969v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is a state-of-the-art technique for 3D scene rendering, offering high efficiency and excellent visual quality. However, be...
arXiv:2603.26638v2 Announce Type: replace Abstract: High-fidelity 3D reconstruction of vehicle exteriors improves buyer confidence in online automotive marketplaces, but fixed multi-camera drive-thro...
AESplat is a new pose‑free feed‑forward 3D Gaussian Splatting framework that improves rendering quality by decoupling view‑independent and view‑dependent appearance modeling. It directly extracts the base view‑independent appearance from input images and predicts higher‑order spherical harmonic coefficients with a shallow MLP that incorporates 3D‑aware inductive biases. Experiments on several datasets show AESplat outperforms state‑of‑the‑art methods, achieving up to 0.8 dB higher PSNR than NAS3R and 1.1 dB over DepthSplat on RealEstate10K.
Tri‑DehazeGS is a Gaussian Splatting framework that decouples clean scene reconstruction from atmospheric haze by representing the scene with Gaussian primitives and the haze medium with an independent view‑shared tri‑plane field. It uses a physical scattering model to compose hazy observations and introduces Medium‑Decoupled Transmittance Gradient Compensation (MD‑TGC) to re‑balance gradients in low‑transmittance regions without altering forward rendering. Experiments on real and synthetic haze benchmarks demonstrate that this approach improves clean novel‑view reconstruction.