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ARXIV:2603.15386 · SPATIAL REASONING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15386SPATIAL REASONINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding.
Opportunity summary
Pain RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception…
Visual Language Models (VLMs) have increasingly become the main paradigm for understanding indoor scenes, but they still struggle with metric and spatial reasoning. Current approaches rely on end-to-end video understanding or large-scale spatial question…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The results we obtain on the static split of VSI-Bench provide an upper bound under ideal perceptual conditions on the spatial reasoning performance, and…
Spatial Reasoning moved forward this cycle; last verified April 2026. Public score 3.0/10.
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RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding.
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10.48550/arXiv.2603.15386RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding.
Abstract
Visual Language Models (VLMs) have increasingly become the main paradigm for understanding indoor scenes, but they still struggle with metric and spatial reasoning. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception and reasoning. In this paper, we investigate whether decoupling perception and reasoning leads to improved spatial reasoning. We propose an agentic framework for static 3D indoor scene reasoning that grounds an LLM in an explicit 3D scene graph (3DSG). Rather than ingesting videos directly, each scene is represented as a persistent 3DSG constructed by a dedicated perception module. To isolate reasoning performance, we instantiate the 3DSG from ground-truth annotations. The agent interacts with the scene exclusively through structured geometric tools that expose fundamental properties such as object dimensions, distances, poses, and spatial relationships. The results we obtain on the static split of VSI-Bench provide an upper bound under ideal perceptual conditions on the spatial reasoning performance, and we find that it is significantly higher than previous works, by up to 16\%, without task specific fine-tuning. Compared to base VLMs, our agentic variant achieves significantly better performance, with average improvements between 33\% to 50\%. These findings indicate that explicit geometric grounding substantially improves spatial reasoning performance, and suggest that structured representations offer a compelling alternative to purely end-to-end visual reasoning.
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PROBLEM
RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception and reas...
METHOD
Visual Language Models (VLMs) have increasingly become the main paradigm for understanding indoor scenes, but they still struggle with metric and spatial reasoning. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, i...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The results we obtain on the static split of VSI-Bench provide an upper bound under ideal perceptual conditions on the spatial reasoning performance, and we find that it is significantly higher than previ...
WHY NOW
Spatial Reasoning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception and reasoning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Visual Language Models (VLMs) have increasingly become the main paradigm for understanding indoor scenes, but they still struggle with metric and spatial reasoning. Current approaches rely on end-to-end video understanding or large-scale spatial question answering fine-tuning, inherently coupling perception and reasoning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The results we obtain on the static split of VSI-Bench provide an upper bound under ideal perceptual conditions on the spatial reasoning performance, and we find that it is significantly higher than previous works, by up to 16\%, without task specific fine-tuning.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Spatial Reasoning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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RieMind proposes a novel framework for enhancing spatial reasoning in indoor scenes through explicit 3D scene graph grounding.
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Spatial Reasoning
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