1 citations · 1 across the 9 of their papers we have counts for
10 papers
NeuroSeg Meets DINOv3: Transferring 2D Self-Supervised Visual Priors to 3D Neuron Segmentation via DINOv3 Initialization
Yik San Cheng, Runkai Zhao, Weidong Cai
2D visual foundation models, such as DINOv3, a self-supervised model trained on large-scale natural images, have demonstrated strong zero-shot generalization, capturing both rich g…
BrainVista: Modeling Naturalistic Brain Dynamics as Multimodal Next-Token Prediction
Xuanhua Yin, Runkai Zhao, Lina Yao +1
Naturalistic fMRI characterizes the brain as a dynamic predictive engine driven by continuous sensory streams. However, modeling the causal forward evolution in realistic neural si…
Improving Multimodal Brain Encoding Model with Dynamic Subject-awareness Routing
Xuanhua Yin, Runkai Zhao, Weidong Cai
Naturalistic fMRI encoding must handle multimodal inputs, shifting fusion styles, and pronounced inter-subject variability. We introduce AFIRE (Agnostic Framework for Multimodal fM…
Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness
Yu Feng, Dingxin Zhang, Runkai Zhao +3
Backdoor attacks pose a severe threat to deep neural networks (DNNs) by implanting hidden backdoors that can be activated with predefined triggers to manipulate model behaviors mal…
CA-W3D: Leveraging Context-Aware Knowledge for Weakly Supervised Monocular 3D Detection
Chupeng Liu, Runkai Zhao, Weidong Cai
Weakly supervised monocular 3D detection, while less annotation-intensive, often struggles to capture the global context required for reliable 3D reasoning. Conventional label-effi…
DINeuro: Distilling Knowledge from 2D Natural Images via Deformable Tubular Transferring Strategy for 3D Neuron Reconstruction
Yik San Cheng, Runkai Zhao, Heng Wang +5
Reconstructing neuron morphology from 3D light microscope imaging data is critical to aid neuroscientists in analyzing brain networks and neuroanatomy. With the boost from deep lea…
Future Does Matter: Boosting 3D Object Detection with Temporal Motion Estimation in Point Cloud Sequences
Rui Yu, Runkai Zhao, Cong Nie +3
Accurate and robust LiDAR 3D object detection is essential for comprehensive scene understanding in autonomous driving. Despite its importance, LiDAR detection performance is limit…
Boosting 3D Neuron Segmentation with 2D Vision Transformer Pre-trained on Natural Images
Yik San Cheng, Runkai Zhao, Heng Wang +2
Neuron reconstruction, one of the fundamental tasks in neuroscience, rebuilds neuronal morphology from 3D light microscope imaging data. It plays a critical role in analyzing the s…
Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development
Runkai Zhao, Yuwen Heng, Heng Wang +5
Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making. However, their application in 3D lane…
PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds
Runkai Zhao, Heng Wang, Chaoyi Zhang +1
Digital neuron reconstruction from 3D microscopy images is an essential technique for investigating brain connectomics and neuron morphology. Existing reconstruction frameworks use…