A collaborative Planning-Perception agent framework using MLLMs constructs a holistic cognitive map through iterative viewpoint supplementation and achieves reported SOTA gains on six 3D benchmarks.
arXiv preprint arXiv:2506.21924 (2025)
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SSR3D-LLM improves fine-grained 3D grounding in unified 3D-LLMs by generating and scoring sequences of latent spatial reasoning steps from the query using fixed Mask3D proposals.
citing papers explorer
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Agentic Collaborative Cognition for Zero-Shot 3D Understanding
A collaborative Planning-Perception agent framework using MLLMs constructs a holistic cognitive map through iterative viewpoint supplementation and achieves reported SOTA gains on six 3D benchmarks.
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SSR3D-LLM: Structured Spatial Reasoning via Latent Steps for Fine-Grained Grounding in Unified 3D-LLMs
SSR3D-LLM improves fine-grained 3D grounding in unified 3D-LLMs by generating and scoring sequences of latent spatial reasoning steps from the query using fixed Mask3D proposals.