4 citations · 18 across the 27 of their papers we have counts for
7 papers · 1 filter
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…
MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps
Jianhao Zheng, Daniel Barath, Marc Pollefeys +1
Creating 3D semantic reconstructions of environments is fundamental to many applications, especially when related to autonomous agent operation (e.g., goal-oriented navigation or o…
"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…