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Found 60 papers, 23 papers with code
GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DSPO layer for bundle adjustment which optimizes the pose and depth of keyframes along with the scale of the monocular depth.
How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling autonomous exploration of unknown environments.
Loopy-SLAM: Dense Neural SLAM with Loop Closures
Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resulting in distorted maps.
Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion
Directly generating scenes from satellite imagery offers exciting possibilities for integration into applications like games and map services.
NeRFmentation: NeRF-based Augmentation for Monocular Depth Estimation
In the case of MDE models for autonomous driving, this issue is exacerbated by the linearity of the captured data trajectories.
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning
The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning.
Auto-Vocabulary Semantic Segmentation
Open-Vocabulary Segmentation (OVS) methods are capable of performing semantic segmentation without relying on a fixed vocabulary, and in some cases, they operate without the need for training or fine-tuning.
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
We present a dense simultaneous localization and mapping (SLAM) method that uses 3D Gaussians as a scene representation.
Revisiting Proposal-based Object Detection
For proposal regression, we solve a simpler problem where we regress to the area of intersection between proposal and ground truth.
ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction
Using these main contributions, our method can enable scenarios with real-time constraints and can scale to arbitrary scene sizes by processing and updating the scene only in a local region defined by the new measurement.
Relational Prior Knowledge Graphs for Detection and Instance Segmentation
Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects.
SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation
The ability to detect objects in images at varying scales has played a pivotal role in the design of modern object detectors.
Learning High-level Semantic-Relational Concepts for SLAM
Concretely, our previous work, Situational Graphs (S-Graphs+), a pioneer in jointly leveraging semantic relationships in the factor optimization process, relies on semantic entities such as Planes and Rooms, whose relationship is mathematically defined.
APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds
To leverage the different properties of each branch, we employ a geometry-aware fusion module that is learned to combine the results of each branch.
Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery
An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system.
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
Estimating camera motion in deformable scenes poses a complex and open research challenge.
UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM
We present an uncertainty learning framework for dense neural simultaneous localization and mapping (SLAM).
R-MAE: Regions Meet Masked Autoencoders
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning.
Learning-based Relational Object Matching Across Views
Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.
Tracking by 3D Model Estimation of Unknown Objects in Videos
We argue that this representation is limited and instead propose to guide and improve 2D tracking with an explicit object representation, namely the textured 3D shape and 6DoF pose in each video frame.
Point-SLAM: Dense Neural Point Cloud-based SLAM
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 cloud that is iteratively generated in an input-dependent data-driven manner.
Human from Blur: Human Pose Tracking from Blurry Images
The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem with a 3D human model, a texture map, and a sequence of poses to describe human motion.
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM
Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM.
Detecting Objects with Context-Likelihood Graphs and Graph Refinement
Contrary to existing methods, which learn objects and relations separately, our key idea is to learn the object-relation distribution jointly.
DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients
Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines.
NeuralMeshing: Differentiable Meshing of Implicit Neural Representations
The generation of triangle meshes from point clouds, i. e. meshing, is a core task in computer graphics and computer vision.
CompNVS: Novel View Synthesis with Scene Completion
We introduce a scalable framework for novel view synthesis from RGB-D images with largely incomplete scene coverage.
Learning Online Multi-Sensor Depth Fusion
Multi-sensor depth fusion is able to substantially improve the robustness and accuracy of 3D reconstruction methods, but existing techniques are not robust enough to handle sensors which operate with diverse value ranges as well as noise and outlier statistics.
Photographic Visualization of Weather Forecasts with Generative Adversarial Networks
This is challenging, because photographic visualizations of weather forecasts should look real, be free of obvious artifacts, and should match the predicted weather conditions.
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM).
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis
Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation.
Ranked #27 on
Monocular Depth Estimation
on KITTI Eigen split
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video.
BoxeR: Box-Attention for 2D and 3D Transformers
Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map.
Non-local Recurrent Regularization Networks for Multi-view Stereo
However, existing recurrent methods only model the local dependencies in the depth domain, which greatly limits the capability of capturing the global scene context along the depth dimension.
RealisticHands: A Hybrid Model for 3D Hand Reconstruction
Estimating 3D hand meshes from RGB images robustly is a highly desirable task, made challenging due to the numerous degrees of freedom, and issues such as self similarity and occlusions.
A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes
This paper presents a real-time online vision framework to jointly recover an indoor scene's 3D structure and semantic label.
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image.
SOLD2: Self-supervised Occlusion-aware Line Description and Detection
We thus hereby introduce the first joint detection and description of line segments in a single deep network.
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes
In this work, we present an unsupervised monocular framework for dense depth estimation of dynamic scenes, which jointly reconstructs rigid and non-rigid parts without explicitly modelling the camera motion.
DeepSurfels: Learning Online Appearance Fusion
We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information.
FMODetect: Robust Detection of Fast Moving Objects
Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.
Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image
For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view.
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i. e. temporal super-resolution).
Ranked #1 on
Video Super-Resolution
on Falling Objects
NeuralFusion: Online Depth Fusion in Latent Space
We present a novel online depth map fusion approach that learns depth map aggregation in a latent feature space.
Weakly Supervised Learning of Multi-Object 3D Scene Decompositions Using Deep Shape Priors
Our approach learns to decompose images of synthetic scenes with multiple objects on a planar surface into its constituent scene objects and to infer their 3D properties from a single view.
Semi-Supervised Learning of Multi-Object 3D Scene Representations
By differentiable rendering, we train our model to decompose scenes self-supervised from RGB-D images.
Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion
Most of the current scene flow methods choose to model scene flow as a per point translation vector without differentiating between static and dynamic components of 3D motion.
KAPLAN: A 3D Point Descriptor for Shape Completion
We present a novel 3D shape completion method that operates directly on unstructured point clouds, thus avoiding resource-intensive data structures like voxel grids.
Online Invariance Selection for Local Feature Descriptors
To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors.
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation
In this paper, we propose a depth completion and uncertainty estimation approach that better handles the challenges of aerial platforms, such as large viewpoint and depth variations, and limited computing resources.
Learned Multi-View Texture Super-Resolution
We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object.
RoutedFusion: Learning Real-time Depth Map Fusion
To this end, we present a novel real-time capable machine learning-based method for depth map fusion.
Learned Semantic Multi-Sensor Depth Map Fusion
Our method learns sensor or algorithm properties jointly with semantic depth fusion and scene completion and can also be used as an expert system, e. g. to unify the strengths of various photometric stereo algorithms.
3D Instance Segmentation via Multi-Task Metric Learning
The second goal is to learn instance information by densely estimating directional information of the instance's center of mass for each voxel.
Ranked #2 on
3D Semantic Instance Segmentation
on ScanNetV2
Learning Priors for Semantic 3D Reconstruction
In contrast to existing variational methods for semantic 3D reconstruction, our model is end-to-end trainable and captures more complex dependencies between the semantic labels and the 3D geometry.
Consensus Maximization for Semantic Region Correspondences
We propose a novel method for the geometric registration of semantically labeled regions.
Fast 3D Reconstruction of Faces With Glasses
We present a method for the fast 3D face reconstruction of people wearing glasses.
Consensus Maximization With Linear Matrix Inequality Constraints
While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers.
Semantically Informed Multiview Surface Refinement
We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes.
Entropy Minimization for Convex Relaxation Approaches
Despite their enormous success in solving hard combinatorial problems, convex relaxation approaches often suffer from the fact that the computed solutions are far from binary and that subsequent heuristic binarization may substantially degrade the quality of computed solutions.
