1 citations · 1 across the 9 of their papers we have counts for
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Long-Tailed 3D Point Cloud Dataset Distillation
Jiahao You, Xu Han, Jinfeng Xu +1
Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving their training utility, enabling efficient 3D point cloud training. Current point…
FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows
Wenzhe He, Meng Wang, JiaWei Qian +3
Existing point-based generative methods for outdoor scenes primarily focus on LiDAR-conditioned completion. During training, noisy point clouds are constructed by perturbing comple…
Towards Pixel-Wise Anomaly Location for High-Resolution PCBA via Self-Supervised Image Reconstruction
Wuyi Liu, Le Jin, Junxian Yang +5
Automated defect inspection of assembled Printed Circuit Board Assemblies (PCBA) is quite challenging due to the insufficient labeled data, micro-defects with just a few pixels in…
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Jinfeng Xu, Xianzhi Li, Yuan Tang +5
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-worl…
MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning
Xu Han, Yuan Tang, Jinfeng Xu +1
We introduce Monarch Sparse Tuning (MoST), the first reparameterization-based parameter-efficient fine-tuning (PEFT) method tailored for 3D representation learning. Unlike existing…
More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding
Yuan Tang, Xu Han, Xianzhi Li +5
Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs…