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Found 16 papers, 4 papers with code
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.
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3D Point Cloud Data Augmentation
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3D Point Cloud Classification
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On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles
Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation.
On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures
To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks.
Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
In this work, we perform the first study to explore the general vulnerability of current LiDAR-based perception architectures and discover that the ignored occlusion patterns in LiDAR point clouds make self-driving cars vulnerable to spoofing attacks.
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks
Since adversarial training (AT) is believed as the most robust defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the 3D model's robustness under AT.
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
In this paper, we systematically study the impact of various self-supervised learning proxy tasks on different architectures and threat models for 3D point clouds with adversarial training.
On The Adversarial Robustness of 3D Point Cloud Classification
Since adversarial training (AT) is believed to be the most effective defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the model's robustness under AT.
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines
To alleviate this issue, we propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data.
Breaking Correlation Shift via Conditional Invariant Regularizer
The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data.
PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition
3D Point cloud is becoming a critical data representation in many real-world applications like autonomous driving, robotics, and medical imaging.
Truthful Transaction Protocol for E-Commerce Networks Based on Double Auction
This screening algorithm is customer-oriented and offers personalized commodities by preventing unqualified sellers from participating in the transaction.
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements.
VPA: Fully Test-Time Visual Prompt Adaptation
Inspired by the success of textual prompting, several studies have investigated the efficacy of visual prompt tuning.
CSI: Enhancing the Robustness of 3D Point Cloud Recognition against Corruption
In this study, we harness the inherent set property of point cloud data to introduce a novel critical subset identification (CSI) method, aiming to bolster recognition robustness in the face of data corruption.
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation
In this paper, we recognize that images share common features in a hierarchical way due to the inherent hierarchical structure of the classification system, which is overlooked by current data parameterization methods.
Dolphins: Multimodal Language Model for Driving
The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness.
