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홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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소개
한국어
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  1. 홈
  2. SOTA
  3. 3D 포인트 클라우드 분류
  4. 3D Point Cloud Classification On Modelnet40 C

3D Point Cloud Classification On Modelnet40 C

평가 지표

Error Rate

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Error Rate
Paper TitleRepository
PointNet0.283PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation-
SimpleView0.271Revisiting Point Cloud Classification with a Simple and Effective Baseline
DGCNN+PointCutMix-R0.173PointCutMix: Regularization Strategy for Point Cloud Classification-
PCT+RSMix0.173Regularization Strategy for Point Cloud via Rigidly Mixed Sample-
PointNet++/+PointMixup0.193PointMixup: Augmentation for Point Clouds-
PCT+PointCutMix-R0.163Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions-
OmniVec0.156OmniVec: Learning robust representations with cross modal sharing-
RSCNN0.262Relation-Shape Convolutional Neural Network for Point Cloud Analysis-
PCT0.255PCT: Point cloud transformer-
PointNet++/+PointCutMix-R0.191PointCutMix: Regularization Strategy for Point Cloud Classification-
PointNet++0.236PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space-
DGCNN0.259Dynamic Graph CNN for Learning on Point Clouds-
OmniVec20.142OmniVec2 - A Novel Transformer based Network for Large Scale Multimodal and Multitask Learning-
0 of 13 row(s) selected.
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소개

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뉴스튜토리얼데이터셋백과사전

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