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Found 42 papers, 23 papers with code
Single-View and Multi-View Depth Fusion
The single and multi-view fusion we propose is challenging in several aspects.
RGBDTAM: A Cost-Effective and Accurate RGB-D Tracking and Mapping System
Simultaneous Localization and Mapping using RGB-D cameras has been a fertile research topic in the latest decade, due to the suitability of such sensors for indoor robotics.
Robotics
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.
Loosely-Coupled Semi-Direct Monocular SLAM
We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods.
Single-View Place Recognition under Seasonal Changes
First we pre-process and propose a partition for the Nordland dataset, frequently used for place recognition research without consensus on the partitions.
Condition-Invariant Multi-View Place Recognition
Visual place recognition is particularly challenging when places suffer changes in its appearance.
Corners for Layout: End-to-End Layout Recovery from 360 Images
The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade.
Closed-Form Optimal Two-View Triangulation Based on Angular Errors
In this paper, we study closed-form optimal solutions to two-view triangulation with known internal calibration and pose.
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Single-view depth estimation suffers from the problem that a network trained on images from one camera does not generalize to images taken with a different camera model.
Triangulation: Why Optimize?
We propose a novel alternative to the classic midpoint method that leads to significantly lower 2D errors and parallax errors.
RGB-D Odometry and SLAM
The two topics are closely related, as the former aims to track the incremental camera motion with respect to a local map of the scene, and the latter to jointly estimate the camera trajectory and the global map with consistency.
Robust Single Rotation Averaging
We propose a novel method for single rotation averaging using the Weiszfeld algorithm.
Geometric Interpretations of the Normalized Epipolar Error
In this work, we provide geometric interpretations of the normalized epipolar error.
Robust Uncertainty-Aware Multiview Triangulation
We propose a robust and efficient method for multiview triangulation and uncertainty estimation.
DOT: Dynamic Object Tracking for Visual SLAM
In this paper we present DOT (Dynamic Object Tracking), a front-end that added to existing SLAM systems can significantly improve their robustness and accuracy in highly dynamic environments.
Rotation-Only Bundle Adjustment
We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure.
Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval
Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem.
Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraints
Estimating a scene reconstruction and the camera motion from in-body videos is challenging due to several factors, e. g. the deformation of in-body cavities or the lack of texture.
Bayesian Deep Neural Networks for Supervised Learning of Single-View Depth
In this paper, we evaluate scalable approaches to uncertainty quantification in single-view supervised depth learning, specifically MC dropout and deep ensembles.
HARA: A Hierarchical Approach for Robust Rotation Averaging
We propose a novel hierarchical approach for multiple rotation averaging, dubbed HARA.
On the Uncertain Single-View Depths in Colonoscopies
Estimating depth information from endoscopic images is a prerequisite for a wide set of AI-assisted technologies, such as accurate localization and measurement of tumors, or identification of non-inspected areas.
Jacobian Computation for Cumulative B-Splines on SE(3) and Application to Continuous-Time Object Tracking
In this paper we propose a method that estimates the $SE(3)$ continuous trajectories (orientation and translation) of the dynamic rigid objects present in a scene, from multiple RGB-D views.
A Model for Multi-View Residual Covariances based on Perspective Deformation
In this work, we derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups.
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization
Place recognition and visual localization are particularly challenging in wide baseline configurations.
Situational Graphs for Robot Navigation in Structured Indoor Environments
Mobile robots should be aware of their situation, comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments.
EndoMapper dataset of complete calibrated endoscopy procedures
Computer-assisted systems are becoming broadly used in medicine.
Medical Image Retrieval
Simultaneous Localization and Mapping
Advanced Situational Graphs for Robot Navigation in Structured Indoor Environments
Mobile robots extract information from its environment to understand their current situation to enable intelligent decision making and autonomous task execution.
SfM-TTR: Using Structure from Motion for Test-Time Refinement of Single-View Depth Networks
Estimating a dense depth map from a single view is geometrically ill-posed, and state-of-the-art methods rely on learning depth's relation with visual appearance using deep neural networks.
What's Wrong with the Absolute Trajectory Error?
This metric is an attractive alternative to the ATE, in that it is capable of discerning the varying trajectory accuracy as the inlier trajectory error or the number of outliers varies.
S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations
In this paper, we present an evolved version of Situational Graphs, which jointly models in a single optimizable factor graph (1) a pose graph, as a set of robot keyframes comprising associated measurements and robot poses, and (2) a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between them.
Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs
Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph).
Ray-Patch: An Efficient Querying for Light Field Transformers
In this paper we propose the Ray-Patch querying, a novel model to efficiently query transformers to decode implicit representations into target views.
DAC: Detector-Agnostic Spatial Covariances for Deep Local Features
Current deep visual local feature detectors do not model the spatial uncertainty of detected features, producing suboptimal results in downstream applications.
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
Estimating camera motion in deformable scenes poses a complex and open research challenge.
LightDepth: Single-View Depth Self-Supervision from Illumination Decline
However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained.
Faster Optimization in S-Graphs Exploiting Hierarchy
A global optimization of the compressed graph is performed every time a loop closure is detected.
Robust Single Rotation Averaging Revisited
In this work, we propose a novel method for robust single rotation averaging that can efficiently handle an extremely large fraction of outliers.
Motion-Bias-Free Feature-Based SLAM
In this paper we show that SLAM commutativity, that is, consistency in trajectory estimates on forward and reverse traverses of the same route, is a significant issue for the state of the art.
Optimal Transport Aggregation for Visual Place Recognition
The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places, relying solely on visual cues.
Ranked #1 on
Visual Place Recognition
on SPED
From Correspondences to Pose: Non-minimal Certifiably Optimal Relative Pose without Disambiguation
Estimating the relative camera pose from $n \geq 5$ correspondences between two calibrated views is a fundamental task in computer vision.
PCR-99: A Practical Method for Point Cloud Registration with 99% Outliers
We propose a robust method for point cloud registration that can handle both unknown scales and extreme outlier ratios.
Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments
Perceptual aliasing and weak textures pose significant challenges to the task of place recognition, hindering the performance of Simultaneous Localization and Mapping (SLAM) systems.
