Search Results for author:
Found 7 papers, 2 papers with code
Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness
Backdoor attacks pose a severe threat to deep neural networks (DNN) by implanting hidden backdoors that can be activated with predefined triggers to manipulate model behaviors maliciously.
DINeuro: Distilling Knowledge from 2D Natural Images via Deformable Tubular Transferring Strategy for 3D Neuron Reconstruction
Reconstructing neuron morphology from 3D light microscope imaging data is critical to aid neuroscientists in analyzing brain networks and neuroanatomy.
Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation
The LiDAR-based 3D object detector that strikes a balance between accuracy and speed is crucial for achieving real-time perception in autonomous driving and robotic navigation systems.
Future Does Matter: Boosting 3D Object Detection with Temporal Motion Estimation in Point Cloud Sequences
Specifically, Motion-Guided Feature Aggregation (MGFA) is proposed to utilize the object trajectory from previous and future motion states to model spatial-temporal correlations into gaussian heatmap over a driving sequence.
Boosting 3D Neuron Segmentation with 2D Vision Transformer Pre-trained on Natural Images
To address this limitation, we aim to distill the consensus knowledge from massive natural image data to aid the segmentation model in learning the complex neuron structures.
Advancements in 3D Lane Detection Using LiDAR Point Clouds: From Data Collection to Model Development
Advanced Driver-Assistance Systems (ADAS) have successfully integrated learning-based techniques into vehicle perception and decision-making.
PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds
In this paper, we propose a novel framework for 3D neuron reconstruction.
