Pith Number

pith:NJEARGBX

pith:2021:NJEARGBX5FYAP5OFJ2DAT5PZJ3

not attested not anchored not stored refs resolved

NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

Christian Theobalt, Lingjie Liu, Peng Wang, Taku Komura, Wenping Wang, Yuan Liu

NeuS learns high-fidelity surfaces as neural signed distance functions by using a volume rendering formulation that removes first-order geometric bias.

arxiv:2106.10689 v3 · 2021-06-20 · cs.CV · cs.GR

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\pithnumber{NJEARGBX5FYAP5OFJ2DAT5PZJ3}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We propose a new formulation that is free of bias in the first order of approximation, thus leading to more accurate surface reconstruction even without the mask supervision.

C2weakest assumption

The new volume rendering formulation eliminates geometric bias without introducing compensating errors or requiring additional constraints beyond the image data.

C3one line summary

NeuS introduces a bias-free volume rendering method for signed distance function representations to reconstruct accurate surfaces from 2D images.

References

54 extracted · 54 resolved · 3 Pith anchors

[1] Sal: Sign agnostic learning of shapes from raw data 2020

[2] Patchmatch: A ran- domized correspondence algorithm for structural image editing 2009

[3] A probabilistic framework for space carving 2001

[4] Z. Chen and H. Zhang. Learning implicit ﬁelds for generative shape modeling. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5932–5941, 2019 2019

[5] 3d-r2n2: A uniﬁed approach for single and multi-view 3d object reconstruction 2016

Formal links

2 machine-checked theorem links

Cited by

30 papers in Pith

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PREF: Phasorial Embedding Fields for Compact Neural Representations

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Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry

SVGS: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors

Receipt and verification

First computed	2026-05-17T23:38:46.710032Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6a48089837e97007f5c54e8609f5f94ecb871edfca0f7dcc0341ac6cf39d23d0

Aliases

arxiv: 2106.10689 · arxiv_version: 2106.10689v3 · doi: 10.48550/arxiv.2106.10689 · pith_short_12: NJEARGBX5FYA · pith_short_16: NJEARGBX5FYAP5OF · pith_short_8: NJEARGBX

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NJEARGBX5FYAP5OFJ2DAT5PZJ3 \
  | 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: 6a48089837e97007f5c54e8609f5f94ecb871edfca0f7dcc0341ac6cf39d23d0

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c1cb897e46a691bdf57e4718b7332b92e3c6fa59a987392e38c9f122c43892e7",
    "cross_cats_sorted": [
      "cs.GR"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2021-06-20T12:59:42Z",
    "title_canon_sha256": "aa90048bef4588513944561c22fca802a78fa7b8e9555d1cdd593cf70c52319f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2106.10689",
    "kind": "arxiv",
    "version": 3
  }
}