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Found 23 papers, 11 papers with code
NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes
Obtaining 3D meshes from neural radiance fields still remains an open challenge since NeRFs are optimized for view synthesis, not enforcing an accurate underlying geometry on the radiance field.
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.
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.
SPARF: Neural Radiance Fields from Sparse and Noisy Poses
Neural Radiance Field (NeRF) has recently emerged as a powerful representation to synthesize photorealistic novel views.
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.
MonoGraspNet: 6-DoF Grasping with a Single RGB Image
6-DoF robotic grasping is a long-lasting but unsolved problem.
SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation
In this paper, to handle these shortcomings, we propose an end-to-end trainable network SSP-Pose for category-level pose estimation, which integrates shape priors into a direct pose regression network.
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories.
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental yet challenging problem in computer vision.
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.
Ranked #1 on
6D Pose Estimation
on LineMOD
(Mean ADD-S metric)
Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection
Monocular 3D object detection continues to attract attention due to the cost benefits and wider availability of RGB cameras.
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.
Object-aware Monocular Depth Prediction with Instance Convolutions
With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography.
Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs
Scene graphs are representations of a scene, composed of objects (nodes) and inter-object relationships (edges), proven to be particularly suited for this task, as they allow for semantic control on the generated content.
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e. g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem.
Ranked #1 on
6D Pose Estimation using RGB
on Occlusion LineMOD
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations.
Ranked #3 on
6D Pose Estimation using RGB
on Occlusion LineMOD
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental problem in computer vision.
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.
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
We present a deep learning method for end-to-end monocular 3D object detection and metric shape retrieval.
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.
Deep Model-Based 6D Pose Refinement in RGB
We present a novel approach for model-based 6D pose refinement in color data.
BOP: Benchmark for 6D Object Pose Estimation
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image.
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot.
Ranked #1 on
6D Pose Estimation using RGBD
on Tejani
