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Found 18 papers, 5 papers with code
BOP Challenge 2020 on 6D Object Localization
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image.
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
There are approximately 39K training and 10K test images from each sensor.
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.
CNOS: A Strong Baseline for CAD-based Novel Object Segmentation
We propose a simple three-stage approach to segment unseen objects in RGB images using their CAD models.
EPOS: Estimating 6D Pose of Objects with Symmetries
A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
Photorealistic Image Synthesis for Object Instance Detection
We present an approach to synthesize highly photorealistic images of 3D object models, which we use to train a convolutional neural network for detecting the objects in real images.
Learning Surrogates via Deep Embedding
The effectiveness of the proposed technique is demonstrated in a post-tuning setup, where a trained model is tuned using the learned surrogate.
Pose Estimation of Specific Rigid Objects
Second, we present HashMatch, an RGB-D method that slides a window over the input image and searches for a match against templates, which are pre-generated by rendering 3D object models in different orientations.
LISA: Learning Implicit Shape and Appearance of Hands
This paper proposes a do-it-all neural model of human hands, named LISA.
Neural Correspondence Field for Object Pose Estimation
Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
UmeTrack: Unified multi-view end-to-end hand tracking for VR
In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space.
In-Hand 3D Object Scanning from an RGB Sequence
As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed.
BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects
In 2022, we witnessed another significant improvement in the pose estimation accuracy -- the state of the art, which was 56. 9 AR$_C$ in 2019 (Vidal et al.) and 69. 8 AR$_C$ in 2020 (CosyPose), moved to new heights of 83. 7 AR$_C$ (GDRNPP).
AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation
To obtain high-quality 3D hand pose annotations for the egocentric images, we develop an efficient pipeline, where we use an initial set of manual annotations to train a model to automatically annotate a much larger dataset.
FoundPose: Unseen Object Pose Estimation with Foundation Features
Pose hypotheses are then generated from 2D-3D correspondences established by matching DINOv2 patch features between the query image and a retrieved template, and finally optimized by featuremetric refinement.
BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects
In the new tasks, methods were required to learn new objects during a short onboarding stage (max 5 minutes, 1 GPU) from provided 3D object models.
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions
In the grasping stage, the model only generates hand motions, whereas in the interaction phase both hand and object poses are synthesized.
