Hugging Face Trending Papers
CrossFit-GS: Out-of-Fold Pose Estimation for COLMAP-Free 3D Gaussian Splatting
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
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
Sep 28
Reliability-Regulated Trajectory Optimization for Progressive COLMAP-Free 3D Gaussian Splatting
The paper introduces a reliability-regulated trajectory optimization framework for progressive COLMAP‑free 3D Gaussian Splatting (3DGS). It uses a self‑supervised bidirectional cycle‑consistency mechanism to control camera trajectory estimation through forward motion propagation and retrospective trajectory correction, thereby reducing error compounding without external priors. Experiments on Tanks and Temples and CO3D‑V2 demonstrate improved camera trajectory accuracy and novel‑view rendering quality compared to existing unposed baselines.
arXiv Computer Vision
6d ago
Frame-Consistent Feed-Forward 3D Gaussian Splatting from Sparse Unposed Images
arXiv:2610.23039v1 Announce Type: new Abstract: Feed-forward Gaussian models have made novel view synthesis almost instantaneous. Feed-forward methods predict Gaussians directly, either in two stages...
arXiv AI
Sep 21
Think Locally, Refine Globally for Memory-Efficient 3D Reconstruction
LoG-VGGT is a memory‑efficient framework for long‑sequence 3D reconstruction that balances local temporal modeling with global camera consistency. It uses cross‑window attention in a small subset of transformer blocks to propagate information across adjacent temporal windows while keeping memory usage bounded. A global camera consistency refinement module further improves long‑horizon pose stability by enforcing scene‑level constraints through cross‑attention between camera and compact register tokens, leading to better depth accuracy and robust camera pose estimation on multiple benchmarks.
arXiv Computer Vision
Aug 25
ORBIT++: Benchmarking SfM in the Wild with 360{\deg} Video
arXiv:2608.22039v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging came...
arXiv Computer Vision
Sep 15
Anchor3R: Streaming 3D Reconstruction with Transient Anchors for Long-Horizon Visual Mapping
Anchor3R is a streaming 3D reconstruction framework that predicts window-relative poses and local geometry in the current‑frame coordinate system, forming a dense relative‑pose graph for online pose updates and loop‑aware motion averaging. It improves long‑horizon pose accuracy and dense reconstruction quality on indoor, outdoor, driving, and RGB‑D benchmarks, and generalizes from 48‑frame training sequences to streams exceeding 10,000 frames while keeping GPU memory bounded. The method addresses issues of train‑test mismatch, early‑anchor bias, and accumulated drift found in previous streaming models.