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pith:4EZO73MZ

pith:2024:4EZO73MZIQYBKJHFPIVBF3FI4Q
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Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry

Kaichen Zhou, Xinhai Chang

EpiS reconstructs surfaces from sparse multi-view images by guiding fine-grained epipolar feature aggregation with coarse cost-volume features.

arxiv:2406.04301 v5 · 2024-06-06 · cs.CV

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Claims

C1strongest claim

EpiS significantly outperforms state-of-the-art generalizable surface reconstruction methods under sparse-view settings, while maintaining strong generalization without per-scene optimization.

C2weakest assumption

That coarse cost-volume features can reliably guide fine-grained epipolar feature aggregation and that a pretrained monocular depth model supplies unbiased scale-invariant global and local constraints that align with the multi-view epipolar geometry (abstract, paragraph on geometry regularization strategy).

C3one line summary

EpiS improves generalizable neural surface reconstruction from sparse views by guiding epipolar feature aggregation with cost volumes, using an epipolar transformer, and applying pretrained monocular depth constraints, outperforming prior methods on DTU and BlendedMVS.

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First computed 2026-08-05T01:36:10.717819Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

e132efed9944301524e57a2a12eca8e424c0cfdda407fd9b0a3881514e37434b

Aliases

arxiv: 2406.04301 · arxiv_version: 2406.04301v5 · doi: 10.48550/arxiv.2406.04301 · pith_short_12: 4EZO73MZIQYB · pith_short_16: 4EZO73MZIQYBKJHF · pith_short_8: 4EZO73MZ
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/4EZO73MZIQYBKJHFPIVBF3FI4Q \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e132efed9944301524e57a2a12eca8e424c0cfdda407fd9b0a3881514e37434b
Canonical record JSON
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    "primary_cat": "cs.CV",
    "submitted_at": "2024-06-06T17:47:48Z",
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