TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception
cs.CV, cs.AI
Submitted: 2026-09-25
Updated: 2026-09-29
Terminology
Sources
- ShelfOcc: Native 3D Supervision beyond LiDAR for Vision-Based Occupancy Estimation
- Submanifold Sparse Convolutional Networks
- DOME: Taming Diffusion Model into High-Fidelity Controllable Occupancy World Model
- FSF-Net: Enhance 4D Occupancy Forecasting with Coarse BEV Scene Flow for Autonomous Driving
- GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving
- GroundNet: Monocular Ground Plane Normal Estimation with Geometric Consistency
- GaussianFusionOcc: A Seamless Sensor Fusion Approach for 3D Occupancy Prediction Using 3D Gaussians
- Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
- SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
- Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
- UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
- OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments
- Self-Supervised Representation Learning with Joint Embedding Predictive Architecture for Automotive LiDAR Object Detection
- Deformable ConvNets v2: More Deformable, Better Results
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