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- Ziwei WangSuggested from co-authorship
- Shan Ting LiuSuggested from co-authorship
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Works14 from public data
- GPA-Net:No-Reference Point Cloud Quality Assessment With Multi-Task Graph Convolutional Network89
To extract effective features for PCQA, a new graph convolution kernel is proposed, i.e., GPAConv, which attentively captures the perturbation of structure and texture, and a multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions).
- Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment37
A novel contrastive pre-training framework tailored for PCQA (CoPA), which enables the pre-trained model to learn quality-aware representations from unlabeled data and a semantic-guided multi-view fusion module to effectively integrate the features of projected images from multiple perspectives.
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- PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment11
This work proposes a self-supervised pre-training framework using masked autoencoders (PAME) to help the model learn useful representations without labels, and outperforms the state-of-the-art NR-PCQA methods on popular benchmarks in terms of prediction accuracy and generalizability.
- Asynchronous Feedback Network for Perceptual Point Cloud Quality Assessment10
This work proposes a novel asynchronous feedback quality prediction network (AFQ-Net), which employs a dual-branch structure to deal with global and local features, simulating the left and right hemispheres of the human brain, and constructs a feedback module between them.
- Point Cloud Compression and Objective Quality Assessment: A Survey6
A comprehensive survey of recent advances in point cloud compression (PCC) and point cloud quality assessment (PCQA) emphasizing their significance for real-time and perceptually relevant applications is provided.
- DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation3
DockAnywhere substantially improves policy success rates and easily generalizes to novel viewpoints from unseen docking points during training, significantly enhancing the generalization capability of mobile manipulation policy in real-world deployment.
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- RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation1
Results demonstrate that a dual-model framework for long-horizon multi-task learning is highly effective, and the strategic utilization of recovery-from-failure curriculum data is a critical insight for successful deployment.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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