Search Results for author:
Found 77 papers, 27 papers with code
PromptVFX: Text-Driven Fields for Open-World 3D Gaussian Animation
Visual effects (VFX) are key to immersion in modern films, games, and AR/VR.
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding
This new dataset and benchmark set a new foundation for OR perception, offering a rich, multimodal resource for next-generation clinical perception.
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments
Operating rooms (ORs) are complex, high-stakes environments requiring precise understanding of interactions among medical staff, tools, and equipment for enhancing surgical assistance, situational awareness, and patient safety.
Ranked #1 on
Video Panoptic Segmentation
on 4D-OR
(using extra training data)
GCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
In this paper, we present GCE-Pose, a method that enhances pose estimation for novel instances by integrating category-level global context prior.
SCRREAM : SCan, Register, REnder And Map:A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark
However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details.
Alignist: CAD-Informed Orientation Distribution Estimation by Fusing Shape and Correspondences
Instead, we propose a pipeline that can leverage correspondence distributions and shape information from the CAD model, which are later used to learn pose distributions.
NeRF-Feat: 6D Object Pose Estimation using Feature Rendering
During inference, CNN is used to predict view-invariant features which can be used to establish correspondences with the implicit 3d model in NeRF.
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion
The scheme ensures that the denoising processes are influenced by a holistic understanding of the scene graph, facilitating the generation of globally coherent scenes.
Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation
Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment.
FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos
However, the setup has been restricted to a static endoscope, limited deformation, or required an external tracking device to retrieve camera pose information of the endoscopic camera.
MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images
MatchU is a generic approach that fuses 2D texture and 3D geometric cues for 6D pose prediction of unseen objects.
HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision.
Deformable 3D Gaussian Splatting for Animatable Human Avatars
ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar.
Reality's Canvas, Language's Brush: Crafting 3D Avatars from Monocular Video
A pose-conditioned deformable NeRF is optimized to volumetrically represent a human subject in canonical T-pose.
S2P3: Self-Supervised Polarimetric Pose Prediction
The novel training paradigm comprises 1) a physical model to extract geometric information of polarized light, 2) a teacher-student knowledge distillation scheme and 3) a self-supervised loss formulation through differentiable rendering and an invertible physical constraint.
RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance
Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology.
SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation
Leveraging the advantage of DINOv2 in providing SE(3)-consistent semantic features, we hierarchically extract two types of SE(3)-invariant geometric features to further encapsulate local-to-global object-specific information.
SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs
In this paper, we present SG-Bot, a novel rearrangement framework that utilizes a coarse-to-fine scheme with a scene graph as the scene representation.
RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy
While most classical methods achieve rotation-equivariant detection and invariant description by design, many learning-based approaches learn to be robust only up to a certain degree.
DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields
To address this challenge, we propose Dynamic Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields (DynaMoN).
On the Localization of Ultrasound Image Slices within Point Distribution Models
We demonstrate that our multi-modal registration framework can localize images on the 3D surface topology of a patient-specific organ and the mean shape of an SSM.
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation.
Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Data
6D pose estimation pipelines that rely on RGB-only or RGB-D data show limitations for photometrically challenging objects with e. g. textureless surfaces, reflections or transparency.
Multi-Modal Dataset Acquisition for Photometrically Challenging Object
This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects.
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction
However, such strategies fail to consistently align the denoised point cloud with the given image, leading to unstable conditioning and inferior performance.
DisguisOR: Holistic Face Anonymization for the Operating Room
Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene.
S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences
Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications.
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data.
Location-Free Scene Graph Generation
Scene Graph Generation (SGG) is a visual understanding task, aiming to describe a scene as a graph of entities and their relationships with each other.
Ranked #3 on
Scene Graph Generation
on 4D-OR
Rotation-Invariant Transformer for Point Cloud Matching
To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task.
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction
Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems.
Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
In our work, we discuss direction-dependent changes in the scene and show that a physics-inspired rendering improves the fidelity of US image synthesis.
Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing
In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes.
TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation
In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images.
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision.
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box.
RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration
More specifically, 3D structures of the whole frame are first represented by our global PPF signatures, from which structural descriptors are learned to help geometric descriptors sense the 3D world beyond local regions.
MonoGraspNet: 6-DoF Grasping with a Single RGB Image
6-DoF robotic grasping is a long-lasting but unsolved problem.
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios.
Ranked #3 on
Object Detection
on OoDIS
Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment
Specifically, we focus on human appearance and learn implicit pose, shape and garment representations of dressed humans from RGB images.
DA$^2$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping
In this paper, we introduce DA$^2$, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects.
CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning
The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods while requiring significantly fewer computations.
BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks
Despite its broad availability, volumetric information acquisition from Bright-Field Microscopy (BFM) is inherently difficult due to the projective nature of the acquisition process.
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
Object pose estimation is crucial for robotic applications and augmented reality.
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
Depth estimation is a core task in 3D computer vision.
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework
We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects.
CroMo: Cross-Modal Learning for Monocular Depth Estimation
Learning-based depth estimation has witnessed recent progress in multiple directions; from self-supervision using monocular video to supervised methods offering highest accuracy.
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Dense methods also improved pose estimation in the presence of occlusion.
Know your sensORs -- A Modality Study For Surgical Action Classification
Existing datasets from OR room cameras are thus far limited in size or modalities acquired, leaving it unclear which sensor modalities are best suited for tasks such as recognizing surgical action from videos.
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision.
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport
Shape matching has been a long-studied problem for the computer graphics and vision community.
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Despite training only on a standard dataset, such as KITTI, augmenting with our vector fields significantly improves the generalization to differently shaped objects and scenes.
Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments
Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price.
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration
We first present a small sequence of RGB-D images displaying a human-object interaction.
ColibriDoc: An Eye-in-Hand Autonomous Trocar Docking System
For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation
A novel temporal attention mechanism further processes the local geometric information in a global context across consecutive images.
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings.
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue.
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration
We study the problem of extracting correspondences between a pair of point clouds for registration.
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration
3D Point clouds are a rich source of information that enjoy growing popularity in the vision community.
I Like to Move It: 6D Pose Estimation as an Action Decision Process
We change this paradigm and reformulate the problem as an action decision process where an initial pose is updated in incremental discrete steps that sequentially move a virtual 3D rendering towards the correct solution.
Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data
Depth completion aims to predict a dense depth map from a sparse depth input.
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching
Feature based visual odometry and SLAM methods require accurate and fast correspondence matching between consecutive image frames for precise camera pose estimation in real-time.
HDD-Net: Hybrid Detector Descriptor with Mutual Interactive Learning
Local feature extraction remains an active research area due to the advances in fields such as SLAM, 3D reconstructions, or AR applications.
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
Contemporary monocular 6D pose estimation methods can only cope with a handful of object instances.
A Multi-Hypothesis Approach to Color Constancy
Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.
SteReFo: Efficient Image Refocusing with Stereo Vision
Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike.
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points.
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.
Markerless Inside-Out Tracking for Interventional Applications
Tracking of rotation and translation of medical instruments plays a substantial role in many modern interventions.
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure.
Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions
Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems.
