53 citations · 227 across the 37 of their papers we have counts for
46 papers
MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping
Zhihao Cao, Hanyu Wu, Li Wa Tang +5
Recent progress in dense SLAM has primarily targeted monocular setups, often at the expense of robustness and geometric coverage. We present MCGS-SLAM, the first purely RGB-based m…
Deblur Gaussian Splatting SLAM
Francesco Girlanda, Denys Rozumnyi, Marc Pollefeys +1
We present Deblur-SLAM, a robust RGB SLAM pipeline designed to recover sharp reconstructions from motion-blurred inputs. The proposed method bridges the strengths of both frame-to-…
Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion
Zuoyue Li, Zhenqiang Li, Zhaopeng Cui +2
Directly generating scenes from satellite imagery offers exciting possibilities for integration into applications like games and map services. However, challenges arise from signif…
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation
Casimir Feldmann, Niall Siegenheim, Nikolas Hars +6
The capabilities of monocular depth estimation (MDE) models are limited by the availability of sufficient and diverse datasets. In the case of MDE models for autonomous driving, th…
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction
Silvan Weder, Francis Engelmann, Johannes L. Schönberger +3
We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly appli…
Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery
Florentin Liebmann, Marco von Atzigen, Dominik Stütz +10
Established surgical navigation systems for pedicle screw placement have been proven to be accurate, but still reveal limitations in registration or surgical guidance. Registration…
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
David Recasens, Martin R. Oswald, Marc Pollefeys +1
Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static sc…
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…
R-MAE: Regions Meet Masked Autoencoders
Duy-Kien Nguyen, Vaibhav Aggarwal, Yanghao Li +4
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning. Inspired by Masked Autoencoding (MAE), a generative pre-…
Learning-based Relational Object Matching Across Views
Cathrin Elich, Iro Armeni, Martin R. Oswald +2
Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reco…
Tracking by 3D Model Estimation of Unknown Objects in Videos
Denys Rozumnyi, Jiri Matas, Marc Pollefeys +2
Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue…
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…
Human from Blur: Human Pose Tracking from Blurry Images
Yiming Zhao, Denys Rozumnyi, Jie Song +3
We propose a method to estimate 3D human poses from substantially blurred images. The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem…
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM
Zihan Zhu, Songyou Peng, Viktor Larsson +4
Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, previous works in this direc…
Detecting Objects with Context-Likelihood Graphs and Graph Refinement
Aritra Bhowmik, Yu Wang, Nora Baka +2
The goal of this paper is to detect objects by exploiting their interrelationships. Contrary to existing methods, which learn objects and relations separately, our key idea is to l…
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
Rémi Pautrat, Daniel Barath, Viktor Larsson +2
Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the stru…
NeuralMeshing: Differentiable Meshing of Implicit Neural Representations
Mathias Vetsch, Sandro Lombardi, Marc Pollefeys +1
The generation of triangle meshes from point clouds, i.e. meshing, is a core task in computer graphics and computer vision. Traditional techniques directly construct a surface mesh…
CompNVS: Novel View Synthesis with Scene Completion
Zuoyue Li, Tianxing Fan, Zhenqiang Li +4
We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage. While generative neural approaches have demonstrated spectacula…
Learning Online Multi-Sensor Depth Fusion
Erik Sandström, Martin R. Oswald, Suryansh Kumar +4
Many hand-held or mixed reality devices are used with a single sensor for 3D reconstruction, although they often comprise multiple sensors. Multi-sensor depth fusion is able to sub…
Photographic Visualization of Weather Forecasts with Generative Adversarial Networks
Christian Sigg, Flavia Cavallaro, Tobias Günther +1
Outdoor webcam images are an information-dense yet accessible visualization of past and present weather conditions, and are consulted by meteorologists and the general public alike…
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis
Zuria Bauer, Zuoyue Li, Sergio Orts-Escolano +3
Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation. In particular, we propose a novel training method split…
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
Zihan Zhu, Songyou Peng, Viktor Larsson +5
Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Neverthele…
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari +1
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a…
BoxeR: Box-Attention for 2D and 3D Transformers
Duy-Kien Nguyen, Jihong Ju, Olaf Booij +2
In this paper, we propose a simple attention mechanism, we call box-attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves…