HiQGS is a query-based hierarchical framework that performs feed-forward Gaussian reconstruction from sparse monocular frames. It introduces a Hierarchical Spatiotemporal Encoder that organizes multi-resolution visual features by image location, observation time, and camera pose, preserving complementary appearance and structural cues. A Time-Adaptive Decoder then uses target timestamp queries to retrieve relevant spatiotemporal evidence and predict the corresponding Gaussian scene state, achieving strong rendering quality and geometric accuracy on static and dynamic scene benchmarks.
arXiv:2610.28120v1 Announce Type: new Abstract: Feedforward reconstruction of dynamic driving scenes requires integrating observations across multiple views and time steps into a coherent 3D represen...
arXiv:2609.15032v1 Announce Type: new Abstract: Live free-viewpoint visualization of real humans is critical for immersive communication and interactive digital experiences. Existing methods either r...
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.
LonGaussian is a streaming feed‑forward framework that reconstructs compact, globally renderable Gaussian scenes from long image sequences with known intrinsics and unknown camera poses. It decodes long‑context geometric features into dense Gaussians, links repeated observations via multi‑frame point tracks, and consolidates predictions with a learned merger. Rendered‑depth consistency filtering removes incorrectly occluding Gaussians, leading to improved novel‑view rendering quality on DL3DV and RealEstate10K benchmarks while using fewer Gaussians than baseline methods.
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...
NoPo4D is a feed‑forward system that reconstructs dynamic 3D scenes from unposed multi‑view videos by using 4D Gaussian splatting. It introduces a velocity decomposition that separates per‑pixel image‑plane shifts from depth changes, enabling supervision with pseudo optical flow and avoiding the need for accurate camera poses or 3D motion ground truth. The method also employs a bidirectional motion encoder and view‑dependent opacity to improve cross‑view and cross‑timestep alignment, achieving state‑of‑the‑art results on multiple benchmarks while being much faster than per‑scene optimization approaches.
Feed-forward 3D Gaussian Splatting (3DGS) enables scalable scene reconstruction without per-scene optimization, yet produces dense Gaussians that are costly to store and transmit. Existing feed-forward Gaussian compression methods formulate decoding as deterministic representation recovery, which becomes inadequate at low bitrates when high-frequency textures and view-dependent appearance are discarded.
arXiv:2607. 00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration.
arXiv:2608.22740v1 Announce Type: new Abstract: Generalizable 3D Gaussian Splatting (G-3DGS) has emerged as a promising approach for novel view synthesis undersparse-view settings. However, existing...
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.
Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time rendering. However, existing methods typically require dense multi-view videos for sufficient geometric constraints, making capture expensive and limiting sparse-camera deployment.
arXiv:2601.15951v2 Announce Type: replace Abstract: Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle t...