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3d

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HilDA: Hierarchical Distillation with Diffusion Advances Self-Supervised LiDAR Pre-Training Technology
Artificial Intelligence #lidar#self-supervised

HilDA: Hierarchical Distillation with Diffusion Advances Self-Supervised LiDAR Pre-Training

Researchers propose HilDA, a self-supervised pretraining framework for LiDAR backbones that uses hierarchical distillation and temporal occupancy diffusion. The method achieves state-of-the-art results on cross-modal distillation benchmarks for 3D object detection, scene flow, and semantic occupancy prediction.

Jun 21, 2026 1 source
Latent Gaussian Splatting Achieves State-of-the-Art in 4D Panoptic Occupancy Tracking for Robots Technology
Artificial Intelligence #latent#gaussian splatting

Latent Gaussian Splatting Achieves State-of-the-Art in 4D Panoptic Occupancy Tracking for Robots

Researchers introduce Latent Gaussian Splatting (LaGS) for 4D Panoptic Occupancy Tracking, a method that models 3D features as sparse Gaussians for continuous spatiotemporal scene understanding. It achieves state-of-the-art results on Occ3D nuScenes and Waymo datasets, addressing limitations of bounding box tracking and static occupancy estimation.

Jun 21, 2026 1 source
AI Supply Chain Galaxy: 3D Visual Analytics Reveals Widespread License Compliance Risks Supply Chain
SC Technology #ai#supply chain

AI Supply Chain Galaxy: 3D Visual Analytics Reveals Widespread License Compliance Risks

AI Supply Chain Galaxy (AISCG) is an interactive 3D visual analytics system for model provenance and compliance auditing. An ecosystem-scale analysis of 908,449 models from Hugging Face revealed that 55.46% exhibit compliance risks or metadata conflicts, with high omission rates in adapter derivations and license drift in fine-tuning.

Jun 16, 2026 1 source
Bayesian 3D Steerable CNNs Combine Equivariance and Uncertainty Quantification Technology
Artificial Intelligence #bayesian#3d

Bayesian 3D Steerable CNNs Combine Equivariance and Uncertainty Quantification

A research paper proposes a Bayesian Steerable-CNN that simultaneously preserves SE(3)-equivariance and enables uncertainty quantification. The model achieves an expected calibration error of 0.0263 and outperforms its deterministic counterpart by up to 6.17% under distributional shift. The framework decomposes uncertainty into epistemic and aleatoric components, with a statistically significant negative correlation between epistemic uncertainty and prediction error.

Jun 16, 2026 1 source
Apple Maps Flyover Upgrade: 3D Cities in Stunning Detail Technology
Software #apple#maps

Apple Maps Flyover Upgrade: 3D Cities in Stunning Detail

Apple Maps is set to receive a significant upgrade in its Flyover feature with the upcoming iOS 27. The enhancement will utilize Vision Intelligence and Gaussian Splatting to provide more detailed 3D city views, potentially surpassing Google Maps' photogrammetry.

Jun 9, 2026 1 source