Pith Number

pith:MULTON7L

pith:2024:MULTON7LSJF7UQ2C6SGB5KVG2C

not attested not anchored not stored refs resolved

Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs

Brandon Smart, Chuanxia Zheng, Iro Laina, Victor Adrian Prisacariu

Splatt3R turns any uncalibrated stereo image pair into a 3D Gaussian splat without camera parameters or depth.

arxiv:2408.13912 v2 · 2024-08-25 · cs.CV · cs.LG

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Claims

C1strongest claim

Given uncalibrated natural images, Splatt3R can predict 3D Gaussian Splats without requiring any camera parameters or depth information... We train Splatt3R on the ScanNet++ dataset and demonstrate excellent generalisation to uncalibrated, in-the-wild images.

C2weakest assumption

That first optimizing only the 3D point cloud geometry loss and then switching to a novel view synthesis objective, combined with the proposed loss masking strategy, reliably avoids local minima that plague direct Gaussian splat training from stereo views.

C3one line summary

Splatt3R is a feed-forward network that predicts 3D Gaussian splats directly from uncalibrated stereo image pairs by extending MASt3R with appearance attributes and a two-stage training procedure.

References

69 extracted · 69 resolved · 0 Pith anchors

[1] The plenoptic func- tion and the elements of early vision 1991

[2] Computational stereo 1982

[3] Mip-nerf 360: Unbounded anti-aliased neural radiance fields 2022

[4] Porf: Pose residual field for accurate neural sur- face reconstruction 2023

[5] Nope-nerf: Optimising neural ra- diance field with no pose prior 2023

Cited by

29 papers in Pith

LangFlash: Feed-forward 3D Language Gaussian Splatting from Sparse Unposed Images

SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis

No Pose, No Problem in 4D: Feed-Forward Dynamic Gaussians from Unposed Multi-View Videos

Generative 3D Gaussians with Learned Density Control

VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold

Receipt and verification

First computed	2026-05-17T23:38:46.006998Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

65173737eb924bfa4342f48c1eaaa6d0879d83747c551af85354ca098416db99

Aliases

arxiv: 2408.13912 · arxiv_version: 2408.13912v2 · doi: 10.48550/arxiv.2408.13912 · pith_short_12: MULTON7LSJF7 · pith_short_16: MULTON7LSJF7UQ2C · pith_short_8: MULTON7L

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/MULTON7LSJF7UQ2C6SGB5KVG2C \
  | 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: 65173737eb924bfa4342f48c1eaaa6d0879d83747c551af85354ca098416db99

Canonical record JSON

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    "abstract_canon_sha256": "b3768bb736e07c4a570b15ed0a1454e7bcd611799b4b23ab0d040f7c0102c20f",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2024-08-25T18:27:20Z",
    "title_canon_sha256": "a1eb89593ad13f9b14c836cfd4e9a5aad90098d153ea00dc22e4c71b2171f402"
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    "kind": "arxiv",
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