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Found 4 papers, 3 papers with code
Multi-modal Situated Reasoning in 3D Scenes
Comprehensive evaluations on MSQA and MSNN highlight the limitations of existing vision-language models and underscore the importance of handling multi-modal interleaved inputs and situation modeling.
An Embodied Generalist Agent in 3D World
However, several significant challenges remain: (i) most of these models rely on 2D images yet exhibit a limited capacity for 3D input; (ii) these models rarely explore the tasks inherently defined in 3D world, e. g., 3D grounding, embodied reasoning and acting.
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.
Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding.
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