arXiv:1505.02890
공식 코드 · Papers with Code
Sparse 3D convolutional neural networks
원문 초록
We have implemented a convolutional neural network designed for processing sparse three-dimensional input data. The world we live in is three dimensional so there are a large number of potential applications including 3D object recognition and analysis of space-time objects. In the quest for efficiency, we experiment with CNNs on the 2D triangular-lattice and 3D tetrahedral-lattice.
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SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Topology-Guided Knowledge Distillation for Efficient Point Cloud Processing
Aligning Text, Images, and 3D Structure Token-by-Token
LM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition
Occlusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition
Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition
토론
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