arXiv Computer Vision
Frame-Consistent Feed-Forward 3D Gaussian Splatting from Sparse Unposed Images
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 8
DeltaSplat: Iterative Gaussian Refinement for Pose-Free Feed-Forward 3D Gaussian Splatting
arXiv:2610.09853v1 Announce Type: new Abstract: Pose-free feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene from sparse, unposed images in a single network pass, removing the need for ca...
arXiv Computer Vision
Oct 7
DensiTok: Making Feed-Forward 3D Gaussian Splatting See More Views Than It Is Given
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...
arXiv Computer Vision
Oct 9
LVSPM: Long Sequence View Synthesis and Pose Estimation Model
LVSPM is a generalizable model that jointly estimates camera poses and synthesizes novel views from uncalibrated image collections. It is trained only with RGB images and pose supervision, avoiding dense 3D ground truth, and uses test‑time training layers to scale to hundreds of input views. On datasets such as RealEstate10k, Co3Dv2, and DL3DV, LVSPM outperforms VGGT in pose estimation across 16–256 views and achieves state‑of‑the‑art pose‑free novel view synthesis quality, maintaining high performance even as scene scale increases.
arXiv Computer Vision
4d ago
CrossFit-GS: Out-of-Fold Pose Estimation for COLMAP-Free 3D Gaussian Splatting
CrossFit-GS is a new framework for pose-free 3D Gaussian Splatting that avoids the need for Structure-from-Motion by directly reconstructing scenes from unposed image sequences. The method partitions the input into interleaved subsets, maintaining two complementary submaps and cross-refining candidate poses against a frozen opposite submap to provide an independent geometric reference. Additional checkerboard validation and spatial coverage safeguards reject unreliable updates, and only verified poses are committed; after refinement, the final global representation is trained from scratch using the fixed refined poses, leading to reduced trajectory drift and improved camera pose estimation and novel-view synthesis across multiple benchmarks.
arXiv Computer Vision
Oct 15
InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Views
arXiv:2610.20341v1 Announce Type: new Abstract: Feed-forward Gaussian reconstruction builds 3D scenes directly from input images for novel view rendering, but learning Gaussian attributes from predic...
arXiv Computer Vision
Oct 16
Benchmarking Efficient & Effective Camera Pose Estimation Strategies for Novel View Synthesis
The paper introduces a benchmark for evaluating Structure-from-Motion (SfM) methods used in novel view synthesis (NVS) such as NeRFs and 3DGS. It compares classical SfM, which relies on local feature matches and bundle adjustment, with recent neural network approaches that regress camera parameters directly for faster but less accurate results. The study finds that reducing feature counts speeds up classical SfM without sacrificing pose accuracy, and that combining neural network initial estimates with classical refinement yields the best balance of efficiency and effectiveness.