The paper introduces BRICE, a Brain-inspired Recurrent Illusory Contour Encoder designed to improve point cloud completion. It leverages a recurrent neural architecture based on Predictive Coding to generate illusory contours, mimicking how the brain completes visual information. BRICE can be integrated into existing completion models and demonstrates performance gains on datasets such as MVP2K, PCN, and PlantPCom.
By Yunlei Wang, Dawei Li, Xue-song Tang
LitePT introduces a lighter yet stronger 3D point cloud backbone that strategically uses convolutional layers in early, high‑resolution stages and switches to attention in deeper, low‑resolution layers. By adding a novel, parameter‑free 3D positional encoding called PointROPE, LitePT preserves spatial layout while reducing redundancy. The resulting model has 3.6× fewer parameters, runs twice as fast, and uses half the memory of the state‑of‑the‑art Point Transformer V3, yet matches or surpasses its performance across multiple tasks and datasets.
By Yuanwen Yue, Damien Robert, Jianyuan Wang, Sunghwan Hong, Jan Dirk Wegner, Christian Rupprecht, Konrad Schindler
arXiv:2609.13991v1 Announce Type: new
Abstract: Reconstructing 3D objects from brain signals offers a promising avenue for understanding human visual cognition. While prior work has shown initial suc...
By Yongyi Lu, Xiongfeng Huang, Zhijing Yang
arXiv:2603. 25157v2 Announce Type: replace Abstract: Recent vision and multimodal foundation backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress, enabling unified modeling across images, text, and beyond.
By Jianfeng Wang, Amine M'Charrak, Luk Koska, Xiangtao Wang, Daniel Petriceanu, Ruizhi Wang, Michael Bumbar, Luca Pinchetti, Thomas Lukasiewicz
PointLearner is a point cloud representation learning network inspired by biological vision. It uses a point-focused attention mechanism that mimics foveal vision by competitively weighting local neighbors against spatially downsampled features extracted via learnable inducing points. Additionally, a context-scan state space emulates eye saccades, inferring global scene structure through a Hilbert‑curve guided scan path, enabling robust performance on various point cloud tasks.
By Kanglin Qu, Pan Gao, Qun Dai, Yuanhao Sun
arXiv:2609.31204v1 Announce Type: cross
Abstract: Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models ar...
By Mo Wang, Wenhao Ye, Zihan Ning, Jiayu Zuo, Junfeng Xia, Hongkai Wen, Quanying Liu
arXiv:2603. 25157v3 Announce Type: replace-cross Abstract: Recent vision backbones, such as Transformer families and state-space models like Mamba, have achieved remarkable progress on image recognition.
By Jianfeng Wang, Amine M'Charrak, Luk Koska, Xiangtao Wang, Daniel Petriceanu, Ruizhi Wang, Michael Bumbar, Luca Pinchetti, Thomas Lukasiewicz
arXiv:2609.23404v1 Announce Type: new
Abstract: Point cloud completion aims to infer a complete 3D shape from a partial point cloud and serves as a fundamental building block for downstream tasks suc...
By Shenghui Wu, Chen Wang, Yuan Feng, Guangshun Wei, Yuanfeng Zhou, Changjian Li
arXiv:2508. 17254v2 Announce Type: replace-cross Abstract: Higher levels of machine intelligence demand alignment with human perception and cognition.
By Xiao Zhang, Kai-Fu Yang, Xian-Shi Zhang, Hong-Zhi You, Hong-Mei Yan, Yong-Jie Li
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.
arXiv:2610.28411v1 Announce Type: cross
Abstract: Deep learning models have become essential tools in medical imaging, yet training them end-to-end for diverse tasks remains challenging due to limite...
By Spiros Maggioros, Guray Erus, Gareth Harman, George Aidinis, Alexander Getka, Aristeidis Sotiras, Christos Davatzikos
arXiv:2606. 24180v1 Announce Type: cross Abstract: Three-dimensional scene completion has evolved as a major problem in computer vision and robotics, and its applications are diverse, including autonomous navigation and augmented reality.
By Afifa Khaled, Said Jadid Abdulkadir, Majdy Mohamed Eltayeb Eltahir