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From the 2 of 7 linked papers with an AI index.

collaborators

7 papers

cs.CV2026

Long-Tailed 3D Point Cloud Dataset Distillation

Jiahao You, Xu Han, Jinfeng Xu +1

The paper introduces a method for distilling large 3D point‑cloud datasets into compact synthetic sets while explicitly handling long‑tailed class distributions, using adaptive syn…

cs.CV2026

FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

Wenzhe He, Meng Wang, JiaWei Qian +3

FPSGen is a framework that generates outdoor point‑cloud scenes by first predicting a bird’s‑eye‑view prior and then using a transport‑flow model to create points, supporting both…

cs.CV2025

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…

cs.CV2025

PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud

Qiao Yu, Xianzhi Li, Yuan Tang +4

Faithfully reconstructing textured meshes is crucial for many applications. Compared to text or image modalities, leveraging 3D colored point clouds as input (colored-PC-to-mesh) o…

cs.CV2025

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…

cs.CV2025

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…