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ARXIV:2603.07660 · 3D SPATIAL UNDERSTANDING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.076603D SPATIAL UNDERSTANDINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Holi-Spatial is a large-scale, automatically generated 3D spatial dataset that significantly improves performance on spatial reasoning tasks, enabling enhanced 3D scene understanding applications.
Opportunity summary
Pain Holi-Spatial is a large-scale, automatically generated 3D spatial dataset that significantly improves performance on spatial reasoning tasks, enabling enhanced 3D scene understanding applications.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Holi-Spatial is a large-scale, automatically generated 3D spatial dataset that significantly improves performance on spatial reasoning tasks, enabling enhanced 3D scene understanding applications. However, existing approaches predominantly construct spatial understanding benchmarks by generating question-answer…
The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding benchmarks by generating question-answer (QA) pairs from a limited number of manually annotated…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets.
3D Spatial Understanding moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Holi-Spatial is a large-scale, automatically generated 3D spatial dataset that significantly improves performance on spatial reasoning tasks, enabling enhanced 3D scene understanding applications.
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10.48550/arXiv.2603.07660Holi-Spatial is a large-scale, automatically generated 3D spatial dataset that significantly improves performance on spatial reasoning tasks, enabling enhanced 3D scene understanding applications.
Abstract
The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding benchmarks by generating question-answer (QA) pairs from a limited number of manually annotated datasets, rather than systematically annotating new large-scale 3D scenes from raw web data. As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets. In this work, we propose Holi-Spatial, the first fully automated, large-scale, spatially-aware multimodal dataset, constructed from raw video inputs without human intervention, using the proposed data curation pipeline. Holi-Spatial supports multi-level spatial supervision, ranging from geometrically accurate 3D Gaussian Splatting (3DGS) reconstructions with rendered depth maps to object-level and relational semantic annotations, together with corresponding spatial Question-Answer (QA) pairs. Following a principled and systematic pipeline, we further construct Holi-Spatial-4M, the first large-scale, high-quality 3D semantic dataset, containing 12K optimized 3DGS scenes, 1.3M 2D masks, 320K 3D bounding boxes, 320K instance captions, 1.2M 3D grounding instances, and 1.2M spatial QA pairs spanning diverse geometric, relational, and semantic reasoning tasks. Holi-Spatial demonstrates exceptional performance in data curation quality, significantly outperforming existing feed-forward and per-scene optimized methods on datasets such as ScanNet, ScanNet++, and DL3DV. Furthermore, fine-tuning Vision-Language Models (VLMs) on spatial reasoning tasks using this dataset has also led to substantial improvements in model performance.
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PROBLEM
Holi-Spatial is a large-scale, automatically generated 3D spatial dataset that significantly improves performance on spatial reasoning tasks, enabling enhanced 3D scene understanding applications. However, existing approaches predominantly construct spatial understanding benchma...
METHOD
The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding benchmarks by generating question-answer (QA) pairs from a limited number of manually annotated dat...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets.
WHY NOW
3D Spatial Understanding moved forward this cycle; last verified April 2026. Public score 8.0/10.
In this work, we propose Holi-Spatial, the first fully automated, large-scale, spatially-aware multimodal dataset, constructed from raw video inputs without human intervention, using the proposed data curation pipeline.
Explicitly stated in the abstract as a primary contribution of the work.
partial
As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets.
Directly stated in the abstract as a limitation of prior work, though specific comparative scalability metrics are not provided.
partial
Holi-Spatial supports multi-level spatial supervision, ranging from geometrically accurate 3D Gaussian Splatting (3DGS) reconstructions with rendered depth maps to object-level and relational semantic annotations, together with corresponding spatial Question-Answer (QA) pairs.
Explicitly listed in the abstract as key components of the dataset.
partial
we further construct Holi-Spatial-4M, the first large-scale, high-quality 3D semantic dataset, containing 12K optimized 3DGS scenes, 1.3M 2D masks, 320K 3D bounding boxes, 320K instance captions, 1.2M 3D grounding instances, and 1.2M spatial QA pairs
Specific quantitative details are provided in the abstract, making this a clear, verifiable claim.
partial
Holi-Spatial demonstrates exceptional performance in data curation quality, significantly outperforming existing feed-forward and per-scene optimized methods on datasets such as ScanNet, ScanNet++, and DL3DV.
Directly stated in the abstract as a result, though specific performance metrics are not provided in the given text.
partial
Furthermore, fine-tuning Vision-Language Models (VLMs) on spatial reasoning tasks using this dataset has also led to substantial improvements in model performance.
Stated as a result in the abstract, but the degree of improvement is not quantified in the provided text.
partial
we further construct Holi-Spatial-4M, the first large-scale, high-quality 3D semantic dataset
Explicitly claimed as a 'first' in the abstract, supported by the description of its novel automated construction.
partial
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Holi-Spatial is a large-scale, automatically generated 3D spatial dataset that significantly improves performance on spatial reasoning tasks, enabling enhanced 3D scene understanding applications.
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3D Spatial Understanding
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