TrustFlip weaponizes consistency-based trust defenses in vehicular collaborative perception by using physical adversarial objects to induce inconsistencies that are misattributed to benign vehicles, leading to their exclusion and reduced system performance.
Pointpillars: Fast encoders for object detection from point clouds
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
baseline 2
citation-polarity summary
years
2026 8verdicts
UNVERDICTED 8roles
baseline 2polarities
baseline 2representative citing papers
RayMamba improves long-range 3D object detection by ray-aligned serialization of sparse voxels for state space modeling, delivering up to 2.49 mAP gain on nuScenes in the 40-50 m range.
HGC-Det applies hyperbolic geometry to constrain cross-modal distillation between images and point clouds, with added semantic-guided voxel optimization and feature aggregation, yielding improved accuracy-efficiency trade-offs on SUN RGB-D, ARKitScenes, KITTI, and nuScenes.
VRS generates annotated roadside LiDAR data from vehicle observations via novel view synthesis with geometry completion and occupancy constraints, improving 3D object detection generalization.
Simulated coordinated IR and LiDAR spoofing achieves 85.5% success deceiving MSF perception on 400 KITTI scenes by creating consistent false 3D objects.
L-PCN exploits spatial locality in point cloud networks via octree partitioning into islands and intra-island hub scheduling, delivering 55-94% less feature fetching, 45-81% less computation, and 1.2-3.2x additional speedup on FPGA prototypes.
SPL unifies unsupervised and sparsely-supervised 3D object detection via semantic pseudo-labeling that produces bounding boxes and point labels, followed by memory-based prototype learning that mines features from both labeled and unlabeled data.
CooperDrive augments autonomous vehicle perception by sharing object-level data from BEV features, enabling earlier conflict anticipation and safer planning with 90 kbps bandwidth and 89 ms latency in real-world NLOS tests.
citing papers explorer
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Adversarial Trust Poisoning in Vehicular Collaborative Perception
TrustFlip weaponizes consistency-based trust defenses in vehicular collaborative perception by using physical adversarial objects to induce inconsistencies that are misattributed to benign vehicles, leading to their exclusion and reduced system performance.
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RayMamba: Ray-Aligned Serialization for Long-Range 3D Object Detection
RayMamba improves long-range 3D object detection by ray-aligned serialization of sparse voxels for state space modeling, delivering up to 2.49 mAP gain on nuScenes in the 40-50 m range.
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Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection
HGC-Det applies hyperbolic geometry to constrain cross-modal distillation between images and point clouds, with added semantic-guided voxel optimization and feature aggregation, yielding improved accuracy-efficiency trade-offs on SUN RGB-D, ARKitScenes, KITTI, and nuScenes.
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Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis
VRS generates annotated roadside LiDAR data from vehicle observations via novel view synthesis with geometry completion and occupancy constraints, improving 3D object detection generalization.
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Cross-Modal Phantom: Coordinated Camera-LiDAR Spoofing Against Multi-Sensor Fusion in Autonomous Vehicles
Simulated coordinated IR and LiDAR spoofing achieves 85.5% success deceiving MSF perception on 400 KITTI scenes by creating consistent false 3D objects.
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L-PCN: A Point Cloud Accelerator Exploiting Spatial Locality through Octree-based Islandization
L-PCN exploits spatial locality in point cloud networks via octree partitioning into islands and intra-island hub scheduling, delivering 55-94% less feature fetching, 45-81% less computation, and 1.2-3.2x additional speedup on FPGA prototypes.
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Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning
SPL unifies unsupervised and sparsely-supervised 3D object detection via semantic pseudo-labeling that produces bounding boxes and point labels, followed by memory-based prototype learning that mines features from both labeled and unlabeled data.
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CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
CooperDrive augments autonomous vehicle perception by sharing object-level data from BEV features, enabling earlier conflict anticipation and safer planning with 90 kbps bandwidth and 89 ms latency in real-world NLOS tests.