The paper introduces Camera Splatting, a novel framework for optimizing camera viewpoints in novel view synthesis. Each camera is represented as a 3D Gaussian (camera splat), and virtual point cameras are positioned near the surface to sample the distribution of these splats. By continuously refining the camera splats to match desired target distributions observed from the point cameras, the method achieves better capture of complex view‑dependent effects such as metallic reflections and detailed textures compared to the Farthest View Sampling approach.
arXiv:2603.20714v4 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become the method of choice for photo-realistic novel view synthesis (NVS), due to its efficiency and compelling v...
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...
The paper introduces Reflection-aware Gaussian Splatting (RGS), a physically-based deferred rendering framework that improves novel view synthesis for reflective objects. RGS leverages a powerful 3D foundation model to provide a strong geometric prior and employs cross-view shape consistency regularization to prevent surface collapse and reduce geometric hollows. Additionally, a reflection-aware densification strategy captures specular variations across views, resulting in higher-quality renderings of reflective objects.
arXiv:2611.07081v1 Announce Type: new Abstract: Multi-perspective-camera systems are widely adopted since they provide broad visual coverage for autonomous driving, motivating the synthesis of a unif...
arXiv:2608.29346v1 Announce Type: new Abstract: 3D Gaussian Splatting has achieved remarkable success in photorealistic rendering, yet it suffers from severe overfitting and geometric artifacts in sp...
The paper introduces a new framework that uses 2D Gaussian Splatting to capture view‑dependent specular effects, especially near‑field reflections, by directly leveraging observable environment information in input images. It combines a hybrid reflection appearance representation with view‑dependent specular modeling, and demonstrates improved rendering quality and geometric accuracy on public benchmarks and a newly curated near‑field reflection dataset.
arXiv:2606. 05124v1 Announce Type: cross Abstract: After the success of 3D Gaussian Splatting (3DGS) for novel view synthesis, many works have explored how to also use it for geometric surface representation.
arXiv:2608.21828v1 Announce Type: new Abstract: Novel-view synthesis of dynamic scenes, crucial for AR/VR applications, remains a challenging problem. Recent methods adapt representations like 3D Gau...
arXiv:2609.12682v1 Announce Type: new Abstract: Reconstructing 3D scenes under real-world low-light conditions remains challenging due to severe sensor noise, low signal-to-noise ratios, and degraded...
OctreeSplat introduces a hierarchical octree-guided feed‑forward Gaussian splatting method that extracts multi‑view features and geometric priors from a frozen visual foundation model. By constructing a hierarchical octree and predicting densification scores, it allocates Gaussian primitives at spatially varying densities, improving depth rendering and surface reconstruction. The approach requires only 76 million additional parameters and 35 GPU‑hours to train, and demonstrates competitive RGB view synthesis, depth synthesis, and surface reconstruction on Replica, TUM, Tanks and Temples, and DTU datasets while reducing the number of Gaussian primitives needed.
arXiv:2609.01516v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction and novel-view synthesis, scenarios with limited input views often lead to poor...
arXiv:2511.16030v3 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) enables efficient, high-fidelity novel view synthesis, yet its performance degrades severely under sparse-view supervi...