arXiv:2508.03077v2 Announce Type: replace Abstract: Feedforward 3D Gaussian Splatting (3DGS) overcomes the limitations of optimization-based 3DGS by enabling fast and high-quality reconstruction with...
arXiv:2610.07958v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene in a single forward pass, replacing per-scene optimization with a network trained across...
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...
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...
arXiv:2604.12251v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) delivers high-fidelity real-time rendering but suffers from geometric and photometric degradations under sparse-view c...
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: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...
The paper presents a training‑free filtering method for feed‑forward 3D Gaussian Splatting that removes transient distractors from 3D reconstructions. By excluding each input’s per‑view Gaussians and re‑rendering the scene, the method identifies inconsistent content through feature similarity and reconstruction error reduction. The approach improves novel‑view quality across multiple models and benchmarks while preserving clean scenes.
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...
FlyMark is a training‑free invisible watermarking method for 3D Gaussian Splatting (3DGS) scenes that embeds a keyed message directly into the scene’s parameter array. It uses directions derived from a published fruit‑fly connectome to encode bits via a dithered quantization‑index‑modulation scheme, achieving high clean bit accuracy and visual fidelity while preserving all geometry and appearance parameters exactly. Extraction requires only simple cone queries, rounding, and majority voting, and the watermark remains robust under a model‑domain threat model on both synthetic and real scenes.
3DGS-HPC is a framework that improves 3D Gaussian Splatting for novel view synthesis by mitigating transient distractors such as moving objects and varying shadows. It combines a patch‑wise classification strategy that uses local spatial consistency for robust region‑level decisions with a hybrid classification metric that adaptively integrates photometric and perceptual cues. Experiments show that this approach outperforms existing methods in reducing distractor effects and enhancing 3DGS quality.
CADSplat is a framework that reconstructs photorealistic, geometrically accurate digital twins from fewer than 15 wide‑baseline images by regularizing 3D Gaussian Splatting with an explicit CAD shape prior. It matches segmented object silhouettes to a CAD library to retrieve a suitable model and camera poses, then anchors Gaussian primitives to the model’s surface and jointly optimizes splat parameters, registration, and a non‑rigid deformation field. Experiments on two real‑world datasets show CADSplat outperforms baselines, especially in sparse and self‑occluded scenarios, and its gains mainly stem from constraining splats to a surface rather than the CAD shape itself.