Search Results for author:
Found 240 papers, 96 papers with code
Handcrafted Outlier Detection Revisited
As a result, outlier detection is a fundamental problem in computer vision and a wide range of approaches, from simple checks based on descriptor similarity to geometric verification, have been proposed over the last decades.
Calibration-free Structure-from-Motion with Calibrated Radial Trifocal Tensors
In this paper we consider the problem of Structure-from-Motion from images with unknown intrinsic calibration.
Privacy Preserving Structure-from-Motion
The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns.
GeoCalib: Learning Single-image Calibration with Geometric Optimization
This single-image calibration can benefit various downstream applications like image editing and 3D mapping.
EgoPressure: A Dataset for Hand Pressure and Pose Estimation in Egocentric Vision
Estimating touch contact and pressure in egocentric vision is a central task for downstream applications in Augmented Reality, Virtual Reality, as well as many robotic applications, because it provides precise physical insights into hand-object interaction and object manipulation.
Space3D-Bench: Spatial 3D Question Answering Benchmark
Answering questions about the spatial properties of the environment poses challenges for existing language and vision foundation models due to a lack of understanding of the 3D world notably in terms of relationships between objects.
P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising
In this work, we tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr\"odinger bridges to points clouds.
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e. g., synthetic data, and an unlabeled target domain.
Learning-based Multi-View Stereo: A Survey
3D reconstruction aims to recover the dense 3D structure of a scene.
Global Structure-from-Motion Revisited
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).
Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information
Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby References
Rendering realistic images from 3D reconstruction is an essential task of many Computer Vision and Robotics pipelines, notably for mixed-reality applications as well as training autonomous agents in simulated environments.
Learning to Make Keypoints Sub-Pixel Accurate
This work addresses the challenge of sub-pixel accuracy in detecting 2D local features, a cornerstone problem in computer vision.
WildGaussians: 3D Gaussian Splatting in the Wild
While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds.
EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting
Human activities are inherently complex, and even simple household tasks involve numerous object interactions.
GLACE: Global Local Accelerated Coordinate Encoding
We propose GLACE, which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network.
Dynamic 3D Gaussian Fields for Urban Areas
We present an efficient neural 3D scene representation for novel-view synthesis (NVS) in large-scale, dynamic urban areas.
OpenDAS: Domain Adaptation for Open-Vocabulary Segmentation
We, therefore, introduce a new task domain adaptation for open-vocabulary segmentation, enhancing VLMs with domain-specific priors while preserving their open-vocabulary nature.
3D Open-Vocabulary Instance Segmentation
Domain Adaptation
+2
NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild
Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing photorealistic views from multi-view images of static scenes, but face challenges in dynamic, real-world environments with distractors like moving objects, shadows, and lighting changes.
3D Neural Edge Reconstruction
Real-world objects and environments are predominantly composed of edge features, including straight lines and curves.
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models
Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world.
NeRF in Robotics: A Survey
Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields.
Retrieval Robust to Object Motion Blur
We propose a method for object retrieval in images that are affected by motion blur.
Efficient Solution of Point-Line Absolute Pose
We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines.
"Where am I?" Scene Retrieval with Language
As such, we need methods that interface between natural language and map representations of the environment.
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds
This allows for accurate detection directly in 3D scenes, object- and environment-aware grasp prediction, as well as robust and repeatable robotic manipulation.
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Our OpenNeRF further leverages NeRF's ability to render novel views and extract open-set VLM features from areas that are not well observed in the initial posed images.
Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning
We propose KYN, a novel method for single-view scene reconstruction that reasons about semantic and spatial context to predict each point's density.
GeneAvatar: Generic Expression-Aware Volumetric Head Avatar Editing from a Single Image
Recently, we have witnessed the explosive growth of various volumetric representations in modeling animatable head avatars.
SceneGraphLoc: Cross-Modal Coarse Visual Localization on 3D Scene Graphs
We introduce a novel problem, i. e., the localization of an input image within a multi-modal reference map represented by a database of 3D scene graphs.
Creating a Digital Twin of Spinal Surgery: A Proof of Concept
Surgery digitalization is the process of creating a virtual replica of real-world surgery, also referred to as a surgical digital twin (SDT).
CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking
To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential.
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
We introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians.
Ranked #1 on
Generalizable Novel View Synthesis
on ACID
F$^3$Loc: Fusion and Filtering for Floorplan Localization
In this paper we propose an efficient data-driven solution to self-localization within a floorplan.
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023.
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.
EgoGen: An Egocentric Synthetic Data Generator
To address this challenge, we introduce EgoGen, a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks.
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.
LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry
Visual odometry estimates the motion of a moving camera based on visual input.
SceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes
Existing 3D scene understanding methods are heavily focused on 3D semantic and instance segmentation.
F3Loc: Fusion and Filtering for Floorplan Localization
In this paper we propose an efficient data-driven solution to self-localization within a floorplan.
Multi-Level Neural Scene Graphs for Dynamic Urban Environments
To this end we present a novel decomposable radiance field approach for dynamic urban environments.
Multiway Point Cloud Mosaicking with Diffusion and Global Optimization
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 scanners or moving RGB-D cameras -- into a unified coordinate system.
Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels
Therefore, we explore the use of image segmentation foundation models to automatically generate training labels for 3D segmentation.
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections
In this work, we propose UniSDF, a general purpose 3D reconstruction method that can reconstruct large complex scenes with reflections.
MuRF: Multi-Baseline Radiance Fields
We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large baselines, and different number of input views).
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.
Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation
Through the hash-based representation, we propose the Spherical Frustum sparse Convolution (SFC) and Frustum Fast Point Sampling (F2PS) to convolve and sample the points stored in spherical frustums respectively.
LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire.
Ranked #1 on
Semantic Segmentation
on Replica
Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change
To this end, we introduce the Nothing Stands Still (NSS) benchmark, which focuses on the spatiotemporal registration of 3D scenes undergoing large spatial and temporal change, ultimately creating one coherent spatiotemporal map.
Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences
Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap.
Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization
As a promising fashion for visual localization, scene coordinate regression (SCR) has seen tremendous progress in the past decade.
Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation
We present a novel approach to perform 3D semantic segmentation solely from 2D supervision by leveraging Neural Radiance Fields (NeRFs).
Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach
However, localizing e. g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes.
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World
Building an interactive AI assistant that can perceive, reason, and collaborate with humans in the real world has been a long-standing pursuit in the AI community.
Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature
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.
Handbook on Leveraging Lines for Two-View Relative Pose Estimation
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.
Volumetric Semantically Consistent 3D Panoptic Mapping
We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating comprehensive, accurate, and efficient semantic 3D maps suitable for autonomous agents in unstructured environments.
CaSAR: Contact-aware Skeletal Action Recognition
Skeletal Action recognition from an egocentric view is important for applications such as interfaces in AR/VR glasses and human-robot interaction, where the device has limited resources.
Learning Disentangled Avatars with Hybrid 3D Representations
Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e. g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata.
ResFields: Residual Neural Fields for Spatiotemporal Signals
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, such as signed distance (SDFs) or radiance fields (NeRFs), via a single multi-layer perceptron (MLP).
R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras
We propose R3D3, a multi-camera system for dense 3D reconstruction and ego-motion estimation.
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
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.
RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation
Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation.
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.
Quantification of Predictive Uncertainty via Inference-Time Sampling
Predictive variability due to data ambiguities has typically been addressed via construction of dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest.
AffineGlue: Joint Matching and Robust Estimation
Moreover, we derive a new minimal solver for homography estimation, requiring only a single affine correspondence (AC) and a gravity prior.
Consensus-Adaptive RANSAC
The proposed attention mechanism and one-step transformer provide an adaptive behavior that enhances the performance of RANSAC, making it a more effective tool for robust estimation.
Lazy Visual Localization via Motion Averaging
Visual (re)localization is critical for various applications in computer vision and robotics.
The Drunkard's Odometry: Estimating Camera Motion in Deforming Scenes
Estimating camera motion in deformable scenes poses a complex and open research challenge.
LightGlue: Local Feature Matching at Light Speed
We introduce LightGlue, a deep neural network that learns to match local features across images.
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving.
Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments
Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments and analyze the influence of camera configurations, training data, and occlusions on the pose accuracy and generalization ability.
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.
SGAligner : 3D Scene Alignment with Scene Graphs
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (ie, unknown overlap -- if any -- and changes in the environment).
Ranked #1 on
Point Cloud Registration
on 3RScan
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.
GlueStick: Robust Image Matching by Sticking Points and Lines Together
Line segments are powerful features complementary to points.
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.
3D Line Mapping Revisited
In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements.
RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration
Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally.
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.
Privacy Preserving Localization via Coordinate Permutations
Recent methods on privacy-preserving image-based localization use a random line parameterization to protect the privacy of query images and database maps.
SGAligner: 3D Scene Alignment with Scene Graphs
We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (i. e., unknown overlap - if any - and changes in the environment).
Four-View Geometry With Unknown Radial Distortion
The minimal case for reconstruction requires 13 points in 4 views for both the calibrated and uncalibrated cameras.
Guiding Local Feature Matching with Surface Curvature
We propose a new method, named curvature similarity extractor (CSE), for improving local feature matching across images.
Removing Objects From Neural Radiance Fields
We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.
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.
VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction.
OpenScene: 3D Scene Understanding with Open Vocabularies
Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision.
Ranked #7 on
3D Open-Vocabulary Instance Segmentation
on Replica
3D Open-Vocabulary Instance Segmentation
3D Semantic Segmentation
+1
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes.
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.
Capturing and Animation of Body and Clothing from Monocular Video
Building on this insight, we propose SCARF (Segmented Clothed Avatar Radiance Field), a hybrid model combining a mesh-based body with a neural radiance field.
IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis
Existing inverse rendering combined with neural rendering methods can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based neural rendering method and can extend its application to room-scale scenes.
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization.
3D Textured Shape Recovery with Learned Geometric Priors
3D textured shape recovery from partial scans is crucial for many real-world applications.
Visual Localization via Few-Shot Scene Region Classification
Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of many computer vision and robotics applications.
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.
NeFSAC: Neurally Filtered Minimal Samples
We tested NeFSAC on more than 100k image pairs from three publicly available real-world datasets and found that it leads to one order of magnitude speed-up, while often finding more accurate results than USAC alone.
Context-Aware Sequence Alignment using 4D Skeletal Augmentation
Temporal alignment of fine-grained human actions in videos is important for numerous applications in computer vision, robotics, and mixed reality.
Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together
Spatial computing -- the ability of devices to be aware of their surroundings and to represent this digitally -- offers novel capabilities in human-robot interaction.
Learning To Find Good Models in RANSAC
We propose the Model Quality Network, MQ-Net in short, for predicting the quality, e. g. the pose error of essential matrices, of models generated inside RANSAC.
Privacy Preserving Partial Localization
We propose a principled approach overcoming these limitations, based on two observations.
Camera Pose Estimation Using Implicit Distortion Models
Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration.
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 #29 on
Monocular Depth Estimation
on KITTI Eigen split
EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices
Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view.
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo
We present IterMVS, a new data-driven method for high-resolution multi-view stereo.
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies
In this work, we propose a novel neural implicit representation for the human body, which is fully differentiable and optimizable with disentangled shape and pose latent spaces.
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.
Learning to Align Sequential Actions in the Wild
To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions.
Controllable Data Augmentation Through Deep Relighting
At the heart of the success of deep learning is the quality of the data.
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.
Reconstructing and grounding narrated instructional videos in 3D
Contrary to the standard scenario of instance-level 3D reconstruction, where identical objects or scenes are present in all views, objects in different instructional videos may have large appearance variations given varying conditions and versions of the same product.
Learning Motion Priors for 4D Human Body Capture in 3D Scenes
To prove the effectiveness of the proposed motion priors, we combine them into a novel pipeline for 4D human body capture in 3D scenes.
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement
Finding local features that are repeatable across multiple views is a cornerstone of sparse 3D reconstruction.
Privacy Preserving Localization and Mapping From Uncalibrated Cameras
In this paper, we propose a solution to the uncalibrated privacy preserving localization and mapping problem.
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.
Shape As Points: A Differentiable Poisson Solver
However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.
H2O: Two Hands Manipulating Objects for First Person Interaction Recognition
To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition.
Ranked #8 on
Action Recognition
on H2O (2 Hands and Objects)
Towards Efficient Graph Convolutional Networks for Point Cloud Handling
In this paper, we aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds.
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.
MBA-VO: Motion Blur Aware Visual Odometry
Motion blur is one of the major challenges remaining for visual odometry methods.
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose
In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms.
Holistic 3D Scene Understanding from a Single Image with Implicit Representation
We not only propose an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.
Ranked #1 on
Monocular 3D Object Detection
on SUN RGB-D
(using extra training data)
Localizing Unsynchronized Sensors with Unknown Sources
We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known.
Orthographic-Perspective Epipolar Geometry
In this paper we consider the epipolar geometry between orthographic and perspective cameras.
The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network
State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer.
DeepSurfels: Learning Online Appearance Fusion
We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information.
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth maps of the immediate surroundings.
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.
DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion
We propose an online multi-view depth prediction approach on posed video streams, where the scene geometry information computed in the previous time steps is propagated to the current time step in an efficient and geometrically plausible way.
PatchmatchNet: Learned Multi-View Patchmatch Stereo
We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo.
Ranked #10 on
Point Clouds
on Tanks and Temples
Cross-Descriptor Visual Localization and Mapping
Visual localization and mapping is the key technology underlying the majority of mixed reality and robotics systems.
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.
Freetures: Localization in Signed Distance Function Maps
Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps.
Robotics
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.
Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research.
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.
HoloLens 2 Research Mode as a Tool for Computer Vision Research
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking
Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust.
Robotics
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.
Infrastructure-based Multi-Camera Calibration using Radial Projections
In particular, our approach is more robust than the naive approach of first estimating intrinsic parameters and pose per camera before refining the extrinsic parameters of the system.
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.
Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings
Many computer vision systems require users to upload image features to the cloud for processing and storage.
AdaLAM: Revisiting Handcrafted Outlier Detection
Local feature matching is a critical component of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization.
Self-Supervised Human Depth Estimation from Monocular Videos
Previous methods on estimating detailed human depth often require supervised training with `ground truth' depth data.
Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction
Modeling hand-object manipulations is essential for understanding how humans interact with their environment.
Ranked #9 on
hand-object pose
on HO-3D v2
OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
In this paper, we present an omnidirectional localization and dense mapping system for a wide-baseline multiview stereo setup with ultra-wide field-of-view (FOV) fisheye cameras, which has a 360 degrees coverage of stereo observations of the environment.
Multi-View Optimization of Local Feature Geometry
In this work, we address the problem of refining the geometry of local image features from multiple views without known scene or camera geometry.
Convolutional Occupancy Networks
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.
Self-Supervised Linear Motion Deblurring
Motion blurry images challenge many computer vision algorithms, e. g, feature detection, motion estimation, or object recognition.
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.
