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.
Mainstream point cloud completion methods are unanimously based on deep learning architectures. On one hand, these methods tend to repetitively stacking attention modules to enhance network performanc...
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.
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.
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...
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.
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.
arXiv:2508. 17254v2 Announce Type: replace-cross Abstract: Higher levels of machine intelligence demand alignment with human perception and cognition.
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...
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:2606. 04772v1 Announce Type: cross Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience.
arXiv:2606. 00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding.
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...