3D Object Classification
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Benchmarks
Most implemented
PointLLM: Empowering Large Language Models to Understand Point Clouds
PointMixer: MLP-Mixer for Point Cloud Understanding
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges
Papers
BrepLLM: Enabling Large Language Models to Understand Boundary Representations
Current token-sequence-based Large Language Models (LLMs) struggle to directly process 3D Boundary Representation (B-rep) models that contain complex geometric and topological information. To this end, we propose BrepLLM…
parameter-efficient fine-tuning3D Object ClassificationContrastive LearningBlendCLIP: Bridging Synthetic and Real Domains for Zero-Shot 3D Object Classification with Multimodal Pretraining
Zero-shot 3D object classification is crucial for real-world applications like autonomous driving, however it is often hindered by a significant domain gap between the synthetic data used for training and the sparse, noi…
3D Object ClassificationAutonomous DrivingDomain AdaptationTACO-Net: Topological Signatures Triumph in 3D Object Classification
3D object classification is a crucial problem due to its significant practical relevance in many fields, including computer vision, robotics, and autonomous driving. Although deep learning methods applied to point clouds…
3D Object ClassificationAutonomous DrivingPoint CloudsReinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments
Adversarial attacks in 3D environments have emerged as a critical threat to the reliability of visual perception systems, particularly in safety-sensitive applications such as identity verification and autonomous driving…
3D Object ClassificationAutonomous DrivingFace RecognitionRW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network
In the domain of 3D object classification, a fundamental challenge lies in addressing the scarcity of labeled data, which limits the applicability of traditional data-intensive learning paradigms. This challenge is parti…
3D Object ClassificationFew-Shot LearningFew-Shot Point Cloud ClassificationPoint Cloud ClassificationPoint-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation
In recent years, the challenge of 3D shape analysis within point cloud data has gathered significant attention in computer vision. Addressing the complexities of effective 3D information representation and meaningful fea…
3D Object ClassificationClassificationScene SegmentationSegmentation