arXiv Computer Vision
Gaussian Density Splatting Network
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arXiv Computer Vision
Sep 4
F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting
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 Computer Vision
2d ago
SplatAnything: Gaussian Spacing Normalization for Feed-forward 3DGS across Arbitrary Scenes
SplatAnything introduces Gaussian Spacing Normalization (GSNorm) to address a limitation in feed-forward 3DGS methods where Gaussian attributes are predicted in a radial-distance-normalized space but the spacing between Gaussian centers varies across scene scales and camera configurations. By canonicalizing a robust scene-level statistic of Gaussian spacing, the method provides a more consistent spatial reference for Gaussian prediction. The authors evaluate the approach on a systematic benchmark covering diverse datasets, input sparsity levels, and challenging extrapolated viewpoints, reporting state‑of‑the‑art performance with strong gains under substantial variations in scene scale and viewpoint.
arXiv Computer Vision
Sep 17
DualCount: Structurally Consistent Density and Point Modeling for Zero-Shot Object Counting
DualCount introduces an instance-aware dual-decoder framework that couples density and point representations for zero‑shot object counting. By treating density estimation as a structured mass allocation over latent object instances, it applies two geometric constraints—per‑instance mass conservation and center‑of‑mass alignment—to enforce instance‑level consistency. Experiments on FSC‑147, PUCPR+, and CARPK demonstrate that this approach consistently reduces counting error and achieves new state‑of‑the‑art performance.
arXiv Computer Vision
Oct 12
CompactSplat: Spatially Adaptive Gaussian Distribution for Feedforward Scene Reconstruction
CompactSplat introduces a feedforward scene reconstruction method that adaptively allocates 3D Gaussian primitives based on texture-guided spatial partitioning and hierarchical sampling. By concentrating dense Gaussians in complex regions and using expanded primitives for flat areas, it reduces redundancy and better captures planar geometry. The framework also allows dynamic sparsity control during inference, enabling trade-offs between rendering quality and memory usage without retraining.
arXiv Computer Vision
Oct 8
Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering
The paper introduces a cluster-based method for predicting dense crowd trajectories, grouping individuals with similar attributes over time to accelerate processing. By replacing individual pedestrian inputs with cluster centroids, the approach reduces computational cost and memory usage while preserving accuracy. Extensive evaluations on challenging dense crowd scenes confirm these benefits compared to state‑of‑the‑art methods.
arXiv Computer Vision
Sep 22
GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting
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....