4 citations · 18 across the 27 of their papers we have counts for
28 papers · 1 filter
Stable and Scalable Bundle Adjustment of Holistic 3D Structures
Shaohui Liu, Rémi Pautrat, Daniel Barath +3
Bundle Adjustment (BA) is a cornerstone of 3D computer vision and has benefited from decades of advances in sparse optimization and numerical methods. It was originally developed f…
AugSplat: Radiance Field-Informed Gaussian Splatting for Sparse-View Settings
Lorenzo Lazzaroni, Riccardo Bollati, Daniel Barath +2
Generating high-quality novel views at real-time frame rates remains a central challenge in 3D vision, particularly in sparse-view scenarios. Neural radiance fields have demonstrat…
SG2Loc: Sequential Visual Localization on 3D Scene Graphs
Nicole Damblon, Olga Vysotska, Federico Tombari +2
Visual localization in complex indoor environments remains a critical challenge for robotics and AR applications. Sequential localization, where pose estimates are refined over tim…
FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos
Alexandros Delitzas, Chenyangguang Zhang, Alexey Gavryushin +8
We present FunRec, a method for reconstructing functional 3D digital twins of indoor scenes directly from egocentric RGB-D interaction videos. Unlike existing methods on articulate…
OVI-MAP:Open-Vocabulary Instance-Semantic Mapping
Zilong Deng, Federico Tombari, Marc Pollefeys +2
Incremental open-vocabulary 3D instance-semantic mapping is essential for autonomous agents operating in complex everyday environments. However, it remains challenging due to the n…
SegSplat: Feed-forward Gaussian Splatting and Open-Set Semantic Segmentation
Peter Siegel, Federico Tombari, Marc Pollefeys +1
We have introduced SegSplat, a novel framework designed to bridge the gap between rapid, feed-forward 3D reconstruction and rich, open-vocabulary semantic understanding. By constru…
YoNoSplat: You Only Need One Model for Feedforward 3D Gaussian Splatting
Botao Ye, Boqi Chen, Haofei Xu +2
Fast and flexible 3D scene reconstruction from unstructured image collections remains a significant challenge. We present YoNoSplat, a feedforward model that reconstructs high-qual…
HouseTour: A Virtual Real Estate A(I)gent
Ata Çelen, Marc Pollefeys, Daniel Barath +1
We introduce HouseTour, a method for spatially-aware 3D camera trajectory and natural language summary generation from a collection of images depicting an existing 3D space. Unlike…
ReSplat: Learning Recurrent Gaussian Splatting
Haofei Xu, Daniel Barath, Andreas Geiger +1
While existing feed-forward Gaussian splatting models offer computational efficiency and can generalize to sparse view settings, their performance is fundamentally constrained by r…
UnLoc: Leveraging Depth Uncertainties for Floorplan Localization
Matthias Wüest, Francis Engelmann, Ondrej Miksik +2
We propose UnLoc, an efficient data-driven solution for sequential camera localization within floorplans. Floorplan data is readily available, long-term persistent, and robust to c…
Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations
Gaia Di Lorenzo, Federico Tombari, Marc Pollefeys +1
Learning effective multi-modal 3D representations of objects is essential for numerous applications, such as augmented reality and robotics. Existing methods often rely on task-spe…
Robust Human Registration with Body Part Segmentation on Noisy Point Clouds
Kai Lascheit, Daniel Barath, Marc Pollefeys +2
Registering human meshes to 3D point clouds is essential for applications such as augmented reality and human-robot interaction but often yields imprecise results due to noise and…
CrossOver: 3D Scene Cross-Modal Alignment
Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys +2
Multi-modal 3D object understanding has gained significant attention, yet current approaches often assume complete data availability and rigid alignment across all modalities. We p…
DepthSplat: Connecting Gaussian Splatting and Depth
Haofei Xu, Songyou Peng, Fangjinhua Wang +4
Gaussian splatting and single-view depth estimation are typically studied in isolation. In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation and…
Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information
Luca Di Giammarino, Boyang Sun, Giorgio Grisetti +3
Accurate localization in diverse environments is a fundamental challenge in computer vision and robotics. The task involves determining a sensor's precise position and orientation,…
Learning to Make Keypoints Sub-Pixel Accurate
Shinjeong Kim, Marc Pollefeys, Daniel Barath
This work addresses the challenge of sub-pixel accuracy in detecting 2D local features, a cornerstone problem in computer vision. Despite the advancements brought by neural network…
Global Structure-from-Motion Revisited
Linfei Pan, Dániel Baráth, Marc Pollefeys +1
Recovering 3D structure and camera motion from images has been a long-standing focus of computer vision research and is known as Structure-from-Motion (SfM). Solutions to this prob…
"Where am I?" Scene Retrieval with Language
Jiaqi Chen, Daniel Barath, Iro Armeni +2
Natural language interfaces to embodied AI are becoming more ubiquitous in our daily lives. This opens up further opportunities for language-based interaction with embodied agents,…
Multiway Point Cloud Mosaicking with Diffusion and Global Optimization
Shengze Jin, Iro Armeni, Marc Pollefeys +1
We introduce a novel framework for multiway point cloud mosaicking (named Wednesday), designed to co-align sets of partially overlapping point clouds -- typically obtained from 3D…
Handbook on Leveraging Lines for Two-View Relative Pose Estimation
Petr Hruby, Shaohui Liu, Rémi Pautrat +2
We propose an approach for estimating the relative pose between calibrated image pairs by jointly exploiting points, lines, and their coincidences in a hybrid manner. We investigat…
Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature
Shengze Jin, Daniel Barath, Marc Pollefeys +1
Point cloud registration has seen recent success with several learning-based methods that focus on correspondence matching and, as such, optimize only for this objective. Following…
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
Rémi Pautrat, Shaohui Liu, Petr Hruby +2
We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal vanishing points, and the unknown focal length of the camera, leveraging a prior vertical direction. Th…
AffineGlue: Joint Matching and Robust Estimation
Daniel Barath, Dmytro Mishkin, Luca Cavalli +3
We propose AffineGlue, a method for joint two-view feature matching and robust estimation that reduces the combinatorial complexity of the problem by employing single-point minimal…
Consensus-Adaptive RANSAC
Luca Cavalli, Daniel Barath, Marc Pollefeys +1
RANSAC and its variants are widely used for robust estimation, however, they commonly follow a greedy approach to finding the highest scoring model while ignoring other model hypot…