arXiv:2607. 17842v1 Announce Type: cross Abstract: Recent breakthroughs in 3D Gaussian Splatting (3DGS) have advanced neural rendering with high fidelity and speed.
arXiv:2506. 07069v2 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, combining high-quality reconstruction with efficient rendering.
Feed-forward 3D Gaussian Splatting enables efficient novel-view synthesis without per-scene optimization, but most existing methods assume a fixed set of context views and process them jointly. This limits their applicability to online scenarios where calibrated views arrive sequentially and the scene must be updated causally.
arXiv:2609.23509v1 Announce Type: new Abstract: Novel view synthesis is a key task for dynamic scene reconstruction, where high rendering speed is essential for applications such as virtual reality....
F4Splat introduces a feed‑forward predictive densification strategy for 3D Gaussian splatting that allocates Gaussians based on a densification‑score guided by spatial complexity and multi‑view overlap. The method predicts per‑region scores to estimate required Gaussian density, enabling explicit control over the total Gaussian budget without retraining. This adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, yielding compact yet high‑quality 3D representations and superior novel‑view synthesis performance with fewer Gaussians.
arXiv:2606. 02068v1 Announce Type: cross Abstract: Recently, novel view synthesis has witnessed remarkable progress, with mainstream methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) delivering impressive results.
arXiv:2609.01306v1 Announce Type: cross Abstract: Triangle-based neural rendering bridges neural scene representations and conventional graphics pipelines by optimizing explicit geometric primitives...
arXiv:2607. 05522v1 Announce Type: cross Abstract: 3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control.
arXiv:2604.18980v2 Announce Type: replace Abstract: Reducing the number of Gaussian-tile pairs is one of the most promising approaches to improve 3D Gaussian Splatting (3D-GS) rendering speed on GPUs...
ReCoSplat is an online feed‑forward Gaussian splatting model that can synthesize novel views from a stream of observations, handling both posed and unposed inputs and optionally using camera intrinsics. It introduces a Render‑and‑Compare module that renders the current scene from the viewpoint of the incoming observation and compares it to the observation, providing a stable conditioning signal to mitigate the mismatch caused by predicted camera poses. A hybrid KV‑cache compression strategy further reduces memory usage, enabling the model to process long sequences efficiently while achieving state‑of‑the‑art performance on online view synthesis tasks.
MeshSplatBench is the first benchmark designed to evaluate triangle- and mesh-based neural rendering methods from native rendering to deployment in graphics engines such as Unity and Blender. It introduces a hierarchical deployment protocol with standard and dedicated options, and a structural audit for mesh splatting to assess topological and geometric integrity. The benchmark’s evaluations show that graphics engine deployment degrades quality, dedicated deployment preserves fidelity at a significant speed cost, and current mesh splatting methods lack sufficient connectivity for manifoldness.
arXiv:2607.20813v2 Announce Type: replace Abstract: Pixel-aligned Gaussian splatting enables efficient and generalizable novel-view synthesis. However, high-resolution rendering faces a critical trad...