Humanoid-OmniOcc delivers a large-scale panoramic stereo occupancy dataset for humanoid robots via Real2Sim2Real, with a model that outperforms monocular baselines in both unseen sim scenes and real settings.
Advancing off-road autonomous driving: The large- scale orad-3d dataset and comprehensive benchmarks
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
Ground4D resolves temporal conflicts in feedforward 4D Gaussian reconstruction for off-road scenes via voxel-grounded temporal aggregation with intra-voxel softmax and surface normal regularization, outperforming prior methods on ORAD-3D and RELLIS-3D while generalizing zero-shot.
Presents the first large-scale infrared off-road dataset and a flow-free temporal model achieving state-of-the-art freespace detection performance with real-time inference.
ReSiReg clusters VLM intermediates into prototypes, derives language descriptors, and reconstructs patches as mixtures to improve spatial consistency in dense language-grounded retrieval for robotics.
citing papers explorer
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Humanoid-OmniOcc: Stereo-Based Full-View Occupancy Dataset for Embodied AI
Humanoid-OmniOcc delivers a large-scale panoramic stereo occupancy dataset for humanoid robots via Real2Sim2Real, with a model that outperforms monocular baselines in both unseen sim scenes and real settings.
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Ground4D: Spatially-Grounded Feedforward 4D Reconstruction for Unstructured Off-Road Scenes
Ground4D resolves temporal conflicts in feedforward 4D Gaussian reconstruction for off-road scenes via voxel-grounded temporal aggregation with intra-voxel softmax and surface normal regularization, outperforming prior methods on ORAD-3D and RELLIS-3D while generalizing zero-shot.
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Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark
Presents the first large-scale infrared off-road dataset and a flow-free temporal model achieving state-of-the-art freespace detection performance with real-time inference.
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ReSiReg: Towards Spatially Consistent Semantics in Language-Conditioned Robotic Tasks
ReSiReg clusters VLM intermediates into prototypes, derives language descriptors, and reconstructs patches as mixtures to improve spatial consistency in dense language-grounded retrieval for robotics.