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ARXIV:2604.05182 · 3D RECONSTRUCTION · SUBMITTED 08 APR · 05:53 UTC · FRESHNESS UNKNOWN
ARXIV:2604.051823D RECONSTRUCTIONSUBMITTED 08 APR · 05:53 UTCFRESHNESS UNKNOWNZhengqin Li · Cheng Zhang · Jakob Engel · Zhao Dong · arXiv
A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art.
Opportunity summary
Pain A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art.
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Blocker Evidence unverified
A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art. Although recent object-centric feed-forward methods deliver robust, high-quality reconstruction, they still lag behind dense-view…
We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows impacts feed-forward 3D reconstruction. Although recent object-centric feed-forward methods deliver robust, high-quality reconstruction, they still lag behind dense-view optimization in…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that expanding the context window -- by substantially increasing the number of active object and image tokens -- remarkably narrows this gap…
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art.
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10.48550/arXiv.2604.05182A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art.
Abstract
We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows impacts feed-forward 3D reconstruction. Although recent object-centric feed-forward methods deliver robust, high-quality reconstruction, they still lag behind dense-view optimization in recovering fine-grained texture and appearance. We show that expanding the context window -- by substantially increasing the number of active object and image tokens -- remarkably narrows this gap and enables high-fidelity 3D object reconstruction and inverse rendering. To scale effectively, we adapt native sparse attention in our architecture design, unlocking its capacity for 3D reconstruction with three key contributions: (1) an efficient coarse-to-fine pipeline that focuses computation on informative regions by predicting sparse high-resolution residuals; (2) a 3D-aware spatial routing mechanism that establishes accurate 2D-3D correspondences using explicit geometric distances rather than standard attention scores; and (3) a custom block-aware sequence parallelism strategy utilizing an All-gather-KV protocol to balance dynamic, sparse workloads across GPUs. As a result, LSRM handles 20x more object tokens and >2x more image tokens than prior state-of-the-art (SOTA) methods. Extensive evaluations on standard novel-view synthesis benchmarks show substantial gains over the current SOTA, yielding 2.5 dB higher PSNR and 40% lower LPIPS. Furthermore, when extending LSRM to inverse rendering tasks, qualitative and quantitative evaluations on widely-used benchmarks demonstrate consistent improvements in texture and geometry details, achieving an LPIPS that matches or exceeds that of SOTA dense-view optimization methods. Code and model will be released on our project page.
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PROBLEM
A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art. Although recent object-centric feed-forward methods deliver robust, high-quality reconstruction, they...
METHOD
We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows impacts feed-forward 3D reconstruction. Although recent object-centric feed-forward methods deliver robust, high-quality reconstruction, they still lag behind dense-view optimizat...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that expanding the context window -- by substantially increasing the number of active object and image tokens -- remarkably narrows this gap and enables high-fidelity 3D object reconstruction and...
WHY NOW
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art. Although recent object-centric feed-forward methods deliver robust, high-quality reconstruction, they still lag behind dense-view optimization in recovering fine-grained texture and appearance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows impacts feed-forward 3D reconstruction. Although recent object-centric feed-forward methods deliver robust, high-quality reconstruction, they still lag behind dense-view optimization in recovering fine-grained texture and appearance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that expanding the context window -- by substantially increasing the number of active object and image tokens -- remarkably narrows this gap and enables high-fidelity 3D object reconstruction and inverse rendering. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
3D Reconstruction moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel 3D reconstruction model that significantly improves fine-grained texture and appearance recovery by scaling transformer context windows, outperforming state-of-the-art.
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3D Reconstruction
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