3D Object Recognition
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Benchmarks
Most implemented
BlenderProc
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition
MVT: Multi-view Vision Transformer for 3D Object Recognition
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
FPNN: Field Probing Neural Networks for 3D Data
Papers
SlimEdge: Performance and Device Aware Distributed DNN Deployment on Resource-Constrained Edge Hardware
Distributed deep neural networks (DNNs) have become central to modern computer vision, yet their deployment on resource-constrained edge devices remains hindered by substantial parameter counts, computational demands, an…
3D Object RecognitionAugmenting cobots for sheet-metal SMEs with 3D object recognition and localisation
Due to high-mix-low-volume production, sheet-metal workshops today are challenged by small series and varying orders. As standard automation solutions tend to fall short, SMEs resort to repetitive manual labour impacting…
3D Object RecognitionHierarchical Abstraction Enables Human-Like 3D Object Recognition in Deep Learning Models
Both humans and deep learning models can recognize objects from 3D shapes depicted with sparse visual information, such as a set of points randomly sampled from the surfaces of 3D objects (termed a point cloud). Although…
3D Object RecognitionSASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Recent advancements in deep learning have greatly enhanced 3D object recognition, but most models are limited to closed-set scenarios, unable to handle unknown samples in real-world applications. Open-set recognition (OS…
3D Object RecognitionObject RecognitionOpen Set LearningAligning Text, Images, and 3D Structure Token-by-Token
Creating machines capable of understanding the world in 3D is essential in assisting designers that build and edit 3D environments and robots navigating and interacting within a three-dimensional space. Inspired by advan…
3D Object RecognitionInstruction FollowingObject RecognitionQuestion AnsweringTopology-Guided Knowledge Distillation for Efficient Point Cloud Processing
Point cloud processing has gained significant attention due to its critical role in applications such as autonomous driving and 3D object recognition. However, deploying high-performance models like Point Transformer V3 …
3D Object RecognitionAutonomous DrivingKnowledge DistillationObject Recognition