3D Object Recognition
27 papers with code • 4 benchmarks • 8 datasets
3D object recognition is the task of recognising objects from 3D data.
Note that there are related tasks you can look at, such as 3D Object Detection which have more leaderboards.
(Image credit: Look Further to Recognize Better)
Benchmarks
These leaderboards are used to track progress in 3D Object Recognition
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Datasets
ModelNet
SHREC
Washington RGB-D
Cube++
DOORS
CY101 Dataset
Corn Seeds Dataset
Latest papers with no code
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation
Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents such as autonomous vehicles and robots alike to operate effectively in unstructured environments.
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition
The proposed model is suitable for open-ended learning scenarios where the number of 3D object categories is not fixed and can grow over time.
Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans
3D object recognition has seen significant advances in recent years, showing impressive performance on real-world 3D scan benchmarks, but lacking in object part reasoning, which is fundamental to higher-level scene understanding such as inter-object similarities or object functionality.
Sparse Depth Completion with Semantic Mesh Deformation Optimization
Sparse depth measurements are widely available in many applications such as augmented reality, visual inertial odometry and robots equipped with low cost depth sensors.
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy.
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects
Our proposed novel self-supervised model learns two types of distinct features: modality-invariant features and modality-specific features.
Spectral Processing and Optimization of Static and Dynamic 3D Geometries
Geometry processing of 3D objects is of primary interest in many areas of computer vision and graphics, including robot navigation, 3D object recognition, classification, feature extraction, etc.
Offboard 3D Object Detection from Point Cloud Sequences
While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
Then, we self-train a representation to capture the intrinsic 3D object representation by decoding 3D transformation parameters from the fused feature representations of multiple views before and after the transformation.
Seeing by haptic glance: reinforcement learning-based 3D object Recognition
Human is able to conduct 3D recognition by a limited number of haptic contacts between the target object and his/her fingers without seeing the object.
