Image-to-3D models successfully generate harmful geometries in most cases with under 0.3% caught by commercial filters; existing safeguards are weak but a stacked defense cuts harmful outputs to under 1% at 11% false-positive cost.
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Rethinking network design and local geometry in point cloud: A simple resid- ual mlp framework
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representative citing papers
RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net architecture with Doppler reparameterization and text alignment for cross-source robustness.
A public benchmark dataset and competition results for 3D dental landmark detection from intraoral scans, with the top team reaching 0.91 rank score using a stratified transformer and DBSCAN.
LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.
MAPR improves adversarial robustness in 3D point cloud networks by aligning latent predictions with intrinsic manifold geometry via curvature/diffusion features and a consistency loss.
Delaunay Canopy uses Delaunay graphs as a geometric prior with region-wise curvature scoring to reconstruct accurate building wireframes from sparse and noisy airborne LiDAR point clouds.
CLAMP pretrains 3D multi-view encoders with contrastive learning on point clouds and actions, then initializes diffusion policies for more sample-efficient fine-tuning on robotic tasks.
The CC-ISAC framework aligns camera visuals with radio echoes via cross-attention and fuses multimodal data to reduce beam steering overhead by 71% and tracking overhead by 1.69-11.15% on the DeepSense 6G dataset while preserving angular accuracy.
Automatically constructed mapping priors from sensor aggregation are integrated via the MPA3D framework to achieve state-of-the-art 3D detection results on the Waymo Open Dataset.
citing papers explorer
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On the Generation and Mitigation of Harmful Geometry in Image-to-3D Models
Image-to-3D models successfully generate harmful geometries in most cases with under 0.3% caught by commercial filters; existing safeguards are weak but a stacked defense cuts harmful outputs to under 1% at 11% false-positive cost.
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RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
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DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition
Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net architecture with Doppler reparameterization and text alignment for cross-source robustness.
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Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge
A public benchmark dataset and competition results for 3D dental landmark detection from intraoral scans, with the top team reaching 0.91 rank score using a stratified transformer and DBSCAN.
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LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
LLaMA-Adapter turns frozen LLaMA 7B into a capable instruction follower using only 1.2M new parameters and zero-init attention, matching Alpaca while extending to image-conditioned reasoning on ScienceQA and COCO.
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Beyond Defenses: Manifold-Aligned Regularization for Intrinsic 3D Point Cloud Robustness
MAPR improves adversarial robustness in 3D point cloud networks by aligning latent predictions with intrinsic manifold geometry via curvature/diffusion features and a consistency loss.
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Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph
Delaunay Canopy uses Delaunay graphs as a geometric prior with region-wise curvature scoring to reconstruct accurate building wireframes from sparse and noisy airborne LiDAR point clouds.
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CLAMP: Contrastive Learning for 3D Multi-View Action-Conditioned Robotic Manipulation Pretraining
CLAMP pretrains 3D multi-view encoders with contrastive learning on point clouds and actions, then initializes diffusion policies for more sample-efficient fine-tuning on robotic tasks.
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A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing
The CC-ISAC framework aligns camera visuals with radio echoes via cross-attention and fuses multimodal data to reduce beam steering overhead by 71% and tracking overhead by 1.69-11.15% on the DeepSense 6G dataset while preserving angular accuracy.
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Scene Reconstruction as Mapping Priors for 3D Detection
Automatically constructed mapping priors from sensor aggregation are integrated via the MPA3D framework to achieve state-of-the-art 3D detection results on the Waymo Open Dataset.