19 citations · 32 across the 8 of their papers we have counts for
10 papers
Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers
Shuhong Zheng, Michael Oechsle, Erik Sandström +3
Visual geometry transformers have become powerful architectures for multi-view 3D reconstruction, enabling joint prediction of multiple 3D attributes in a feed-forward manner. Howe…
ProDyG: Progressive Dynamic Scene Reconstruction via Gaussian Splatting from Monocular Videos
Shi Chen, Erik Sandström, Sandro Lombardi +2
Achieving truly practical dynamic 3D reconstruction requires online operation, global pose and map consistency, detailed appearance modeling, and the flexibility to handle both RGB…
LoopSplat: Loop Closure by Registering 3D Gaussian Splats
Liyuan Zhu, Yue Li, Erik Sandström +3
Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian Splats (3DGS) has recently shown promise towards more accurate, dense 3D scene maps. However, existing 3DGS-based…
VF-NeRF: Learning Neural Vector Fields for Indoor Scene Reconstruction
Albert Gassol Puigjaner, Edoardo Mello Rella, Erik Sandström +2
Implicit surfaces via neural radiance fields (NeRF) have shown surprising accuracy in surface reconstruction. Despite their success in reconstructing richly textured surfaces, exis…
Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians
Erik Sandström, Keisuke Tateno, Michael Oechsle +4
3D Gaussian Splatting has emerged as a powerful representation of geometry and appearance for RGB-only dense Simultaneous Localization and Mapping (SLAM), as it provides a compact…
GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
Ganlin Zhang, Erik Sandström, Youmin Zhang +3
Recent advancements in RGB-only dense Simultaneous Localization and Mapping (SLAM) have predominantly utilized grid-based neural implicit encodings and/or struggle to efficiently r…
Loopy-SLAM: Dense Neural SLAM with Loop Closures
Lorenzo Liso, Erik Sandström, Vladimir Yugay +2
Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resultin…
UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM
Erik Sandström, Kevin Ta, Luc Van Gool +1
We present an uncertainty learning framework for dense neural simultaneous localization and mapping (SLAM). Estimating pixel-wise uncertainties for the depth input of dense SLAM me…
Point-SLAM: Dense Neural Point Cloud-based SLAM
Erik Sandström, Yue Li, Luc Van Gool +1
We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point clou…
OOWL500: Overcoming Dataset Collection Bias in the Wild
Brandon Leung, Chih-Hui Ho, Amir Persekian +5
The hypothesis that image datasets gathered online "in the wild" can produce biased object recognizers, e.g. preferring professional photography or certain viewing angles, is studi…