Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.
By Xuanzhi Liu, Yuhe Zhou, Xinyi Wu, Zhenyao Wu, Jinghao Chen, Ruize Han, Song Wang
RoGe is a new end‑to‑end framework for novel view synthesis that jointly learns an implicit 3D scene representation and a video diffusion model. It eliminates the need for explicit 3D intermediates by querying the implicit scene with camera rays to produce geometric features that condition the diffusion model. Experiments on DL3DV show that RoGe surpasses reconstruction‑based, generation‑based, and hybrid baselines in image quality and temporal consistency, and ablations confirm the benefits of ray‑queried features and joint training.
By Xiaolei Lang, Ze Kang, Zehao Huang, Naiyan Wang
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views are sparse or target v...
arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.
By Zhengqin Li, Cheng Zhang, Jakob Engel, Zhao Dong
While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction and novel-view synthesis, scenarios with limited input views often lead to poor reconstruction quality and artifacts in rendered...
arXiv:2608.23549v1 Announce Type: new
Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
By Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan
arXiv:2607. 00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity.
By Kangmin Seo, Sangeek Hyun, MinKyu Lee, Jae-Pil Heo
Dynamic-Robust Photometric-Semantic Reconstruction for Open-Vocabulary 3D Scene Understanding introduces SPAR, a joint semantic‑geometric encoding architecture that isolates transient dynamic noise before latent space aggregation. The method couples motion estimation with multi‑view visual and semantic learning in a dynamic‑region‑aware end‑to‑end training paradigm, enabling the network to resolve motion conflicts and produce temporally stable scene representations. Experiments on the D‑RE10K benchmark show state‑of‑the‑art performance, achieving high PSNR values for novel view synthesis and an 88.5% mIoU for motion mask prediction in a self‑supervised setting.
By Boyu Cai, Li Yang, Yan Xu, Wei Liu, Nian Liu, Sikui Zhang, Yan Wang, Chunfeng Yuan, Weiming Hu
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...
By Qian Wang, Yu Wang, Weiqi Li, Xinhua Cheng, Xiandong Meng, Ronggang Wang, Jian Zhang
UniQueR is a unified query‑based feedforward framework that reconstructs 3D scenes from unposed images by treating reconstruction as a sparse 3D query inference problem. It learns a compact set of 3D anchor points that serve as explicit geometric queries, allowing the network to infer scene structure—including occluded geometry—in a single forward pass. By encoding spatial and appearance priors directly in global 3D space and using a decoupled cross‑attention design, UniQueR achieves strong geometric expressiveness while reducing memory and computational cost, outperforming state‑of‑the‑art feedforward methods on Mip‑NeRF 360 and VR‑NeRF with far fewer primitives.
By Chensheng Peng, Quentin Herau, Jiezhi Yang, Yichen Xie, Yihan Hu, Wenzhao Zheng, Matthew Strong, Masayoshi Tomizuka, Wei Zhan
arXiv:2609.18034v1 Announce Type: new
Abstract: Novel view synthesis from unposed multi-view images remains challenging, as the model must jointly learn scene representations and camera parameters wi...
By Wenyu Li, Sidun Liu, Peng Qiao, Yong Dou, Tongrui Hu
arXiv:2608.28895v1 Announce Type: new
Abstract: We introduce ReconSplat, a feed-forward model for 3D scene reconstruction that aims to address the longstanding trade-off between plausible view genera...
By Giuseppe Stracquadanio, Kevin Raj, Julia Grabinski, Stefan Roth