arXiv Computer Vision
DimFlow: Bidirectional Grid-Expert Learning for Dynamic Gaussian Reconstruction
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arXiv Computer Vision
Oct 27
VGGT-$\Omega$
arXiv:2605.15195v2 Announce Type: replace Abstract: Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also prov...
arXiv Computer Vision
Sep 17
Generalizable Neural Reconstruction of High-Fidelity Surfaces via Sparse Volumetric Representations
The paper introduces SVRecon, a generalizable neural surface reconstruction framework that uses sparse volumetric representations to achieve high-resolution 3D reconstruction. It employs a two-stage architecture: first predicting occupied voxels with an occupancy network, then rendering only within those regions using specialized sparse algorithms. This approach allows reconstruction at resolutions up to 512³ on 32 GB hardware, producing smoother and more precise surfaces, especially in sparse-view scenarios.
arXiv Computer Vision
Sep 30
EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
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...
arXiv Computer Vision
Sep 22
GrapeSplat: Geometry-Grounded Reconstruction via Amalgamated Pose-Free Encoding for Feed-Forward 3D Gaussian Splatting
arXiv:2609.23182v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting now reconstructs renderable scenes from unposed, uncalibrated images. Yet, most models supervise only photometric co...
arXiv Computer Vision
Sep 15
Tele360: Real-Time Feed-Forward Human Reconstruction from Sparse Unposed Cameras
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...
arXiv Computer Vision
Aug 28
ReSplat: Learning Recurrent Gaussian Splatting
ReSplat introduces a recurrent Gaussian splatting model that iteratively refines 3D Gaussians using the rendering error as a feedback signal, avoiding explicit gradient computation. The method starts from a compact reconstruction in a subsampled space, producing far fewer Gaussians than prior per‑pixel models, which reduces computational cost. Experiments on multiple datasets, view counts, and resolutions show state‑of‑the‑art performance with faster rendering speeds.