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arxiv: 2605.16258 · v2 · pith:4EWKSBWOnew · submitted 2026-05-15 · 💻 cs.CV · cs.AI· cs.RO

IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation

Yuqi Wu , Tianyu Hu , Wenzhao Zheng , Yuanhui Huang , Haowen Sun , Jie Zhou , Jiwen Lu This is my paper

Pith reviewed 2026-05-22 09:16 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.RO
keywords implicit neural representationvisual geometry transformerpose-free reconstructionsigned distance functionneural scene representationnovel view synthesismulti-view geometrycontinuous geometry
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The pith

IVGT implicitly models continuous and coherent geometry from pose-free multi-view images by learning a continuous neural scene representation in a canonical coordinate system.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that an implicit visual geometry transformer can reconstruct coherent 3D geometry and appearance directly from unposed multi-view images. Instead of regressing explicit pointmaps, the method learns a continuous neural scene representation in a canonical coordinate system. This representation supports continuous spatial queries at any 3D position to predict signed distance values and colors through lightweight decoders. If correct, the approach would allow direct extraction of continuous surfaces and rendering of images, depth, and normals from arbitrary viewpoints. It trains through joint optimization on multiple datasets using only 2D supervision plus 3D geometric regularization, leading to generalization across scenes and tasks such as mesh reconstruction and novel view synthesis.

Core claim

IVGT learns a continuous neural scene representation in a canonical coordinate system from pose-free multi-view images. It retrieves local features to predict signed distance (SDF) values and colors using lightweight decoders. This enables direct extraction of continuous and coherent surface geometry along with rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints. Training occurs via multi-dataset joint optimization with 2D image supervision and 3D geometric regularization, yielding generalization across scenes on tasks including mesh reconstruction, novel view synthesis, depth estimation, surface normal estimation, and camera pose estimation.

What carries the argument

The Implicit Visual Geometry Transformer that maps pose-free images into a canonical neural scene field supporting continuous spatial queries for SDF and color prediction via lightweight decoders.

Load-bearing premise

Joint optimization across multiple datasets using only 2D image supervision plus 3D geometric regularization suffices to learn a generalizable canonical representation without introducing inconsistencies or requiring pose information.

What would settle it

Reconstruct a mesh from a held-out set of unposed images and check whether nearby 3D queries produce consistent SDF values without surface discontinuities or artifacts relative to ground-truth geometry.

Figures

Figures reproduced from arXiv: 2605.16258 by Haowen Sun, Jie Zhou, Jiwen Lu, Tianyu Hu, Wenzhao Zheng, Yuanhui Huang, Yuqi Wu.

Figure 1
Figure 1. Figure 1: IVGT implicitly models coherent 3D geometry and appearance from pose-free multi-view images in one feedforward pass. It learns a continuous neural scene representation in a global canonical coordinate system, enabling various downstream tasks including mesh reconstruction, novel-view synthesis, and surface estimation across diverse scenes. ABSTRACT Reconstructing coherent 3D geometry and appearance from un… view at source ↗
Figure 2
Figure 2. Figure 2: Explicit vs. implicit visual geometry paradigms. Existing explicit models decode per￾pixel 3D point coordinates for each input view independently, producing discrete and view-indexed pointmaps. Our implicit model instead learns a continuous 3D field from which any spatial location can be queried, enabling direct surface extraction and novel view rendering without post-processing. et al., 2025a; Zhuo et al.… view at source ↗
Figure 3
Figure 3. Figure 3: Framework of IVGT. Our model takes pose-free multi-view images as input and encodes them into per-view features, which are aggregated via global feature attention into a unified scene representation in a canonical coordinate system. This representation supports continuous 3D queries, where cascaded decoders predict SDF and colors for volume rendering and surface extraction.We additionally decode camera pos… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative mesh reconstruction on ScanNet. IVGT produces geometrically complete and surface-coherent meshes in a single forward pass, achieving comparable or superior reconstruction quality to per-scene optimization baselines [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Colored mesh reconstruction on diverse scenes and objects. IVGT generalizes across indoor scenes and objects of varying scale, producing geometrically complete and visually consistent colored meshes without any test-time optimization. and MonoSDF (Yu et al., 2022). In [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Mesh vs. pointmap representations. Pixel-aligned pointmap reconstruction suffers from sparsity and surface discontinuities, especially at object boundaries. Meshes and points extracted from IVGT exhibit significantly improved geometric continuity and completeness, validating the advantage of continuous implicit geometry over discrete explicit representations. et al., 2014) in [PITH_FULL_IMAGE:figures/full… view at source ↗
Figure 7
Figure 7. Figure 7: Novel view synthesis on ScanNet. Given pose-free multi-view inputs, IVGT renders RGB images, depth maps, and surface normal maps from unseen viewpoints. The outputs are visually coherent and geometrically smooth, confirming that the unified implicit representation captures both appearance and 3D structure. All three modalities are derived from the same SDF field without any task-specific decoding heads, de… view at source ↗
Figure 8
Figure 8. Figure 8: Effect of the two-stage training strategy. The first stage (2D supervision only) captures coarse geometry but produces rough, irregular surfaces. Adding Eikonal and smoothness regulariza￾tion in the second stage significantly improves surface quality, yielding smooth and coherent meshes. rely on absolute coordinates and achieve limited performance, likely due to ambiguity introduced by reference-frame-depe… view at source ↗
read the original abstract

Reconstructing coherent 3D geometry and appearance from unposed multi-view images is a fundamental yet challenging problem in computer vision. Most existing visual geometry foundation models predict explicit geometry by regressing pixel-aligned pointmaps, often suffering from redundancy and limited geometric continuity. We propose IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images. This formulation learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions, retrieving local features to predict signed distance (SDF) values and colors using lightweight decoders. It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints. We train IVGT via multi-dataset joint optimization with 2D supervision and 3D geometric regularization. IVGT demonstrates generalization across scenes and achieves strong performance on various tasks, including mesh and point cloud reconstruction, novel view synthesis, depth and surface normal estimation, and camera pose estimation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images by learning a continuous neural scene representation in a canonical coordinate system. This allows continuous spatial queries at any 3D positions to predict signed distance (SDF) values and colors using lightweight decoders. The model is trained via multi-dataset joint optimization with 2D supervision and 3D geometric regularization, and is shown to generalize across scenes with strong performance on mesh and point cloud reconstruction, novel view synthesis, depth and surface normal estimation, and camera pose estimation.

Significance. If the results hold, the work is significant for advancing neural scene representations beyond explicit pointmap methods by providing an implicit, continuous alternative that supports coherent geometry extraction and multi-task performance without pose information. The joint optimization approach and canonical frame are key innovations that could enable more generalizable 3D vision models. The manuscript provides evidence through various task evaluations, though further validation of the canonical consistency would strengthen the claims.

major comments (2)
  1. [§3.2] §3.2 (Training Objective): The 3D geometric regularization is presented as sufficient to learn a consistent canonical coordinate system from pose-free inputs, but the paper does not report experiments measuring SDF prediction variance for the same 3D query point when using different subsets of input views or across datasets. This is load-bearing for the central claim of coherent geometry without pose cues.
  2. [Table 4] Table 4 (Quantitative Comparisons): The reported gains in mesh reconstruction and normal estimation over explicit pointmap baselines are promising, but without an explicit cross-view coherence metric (such as variance in rendered depth or normals from held-out view combinations), it remains unclear whether the implicit canonical formulation delivers the claimed continuity advantage.
minor comments (2)
  1. [Figure 2] Figure 2 (Architecture Diagram): The illustration of feature retrieval from the canonical frame and the lightweight decoders would benefit from additional annotations or arrows to clarify the query process at arbitrary 3D positions.
  2. [Related Work] Related Work section: The discussion of prior implicit representations could be strengthened by citing additional recent extensions of NeRF-style methods for geometry prediction to better position the contribution relative to the field.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential significance of IVGT for advancing implicit neural scene representations. We address the two major comments below and will incorporate revisions to strengthen the validation of canonical consistency and cross-view coherence.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Training Objective): The 3D geometric regularization is presented as sufficient to learn a consistent canonical coordinate system from pose-free inputs, but the paper does not report experiments measuring SDF prediction variance for the same 3D query point when using different subsets of input views or across datasets. This is load-bearing for the central claim of coherent geometry without pose cues.

    Authors: We agree that direct quantification of SDF variance across view subsets would provide stronger empirical support for the consistency of the learned canonical coordinate system. In the revised manuscript we will add a targeted ablation in Section 3.2 (and supplementary material) that fixes 3D query points and measures SDF prediction variance when the model is conditioned on random subsets of input views drawn from the same scene as well as across different training datasets. These results will be obtained by re-using the already-trained model checkpoints and will be reported alongside the existing training objective description. revision: yes

  2. Referee: [Table 4] Table 4 (Quantitative Comparisons): The reported gains in mesh reconstruction and normal estimation over explicit pointmap baselines are promising, but without an explicit cross-view coherence metric (such as variance in rendered depth or normals from held-out view combinations), it remains unclear whether the implicit canonical formulation delivers the claimed continuity advantage.

    Authors: We acknowledge that an explicit coherence metric would make the continuity benefit of the implicit canonical formulation more transparent. In the revised version we will augment Table 4 (or add a companion table) with a cross-view coherence metric: for each test scene we will render depth and surface normals from multiple held-out view combinations, compute the per-pixel variance across those renderings, and report mean variance for both IVGT and the explicit pointmap baselines. This addition will be computed from the same evaluation protocol already used in the paper and will directly quantify the claimed advantage. revision: yes

Circularity Check

0 steps flagged

No significant circularity in IVGT derivation chain

full rationale

The paper defines IVGT as an implicit transformer that learns a continuous neural scene representation in a canonical coordinate system from pose-free multi-view images, trained via multi-dataset joint optimization using 2D image supervision and 3D geometric regularization. No equations, self-citations, or procedural steps in the abstract or described claims reduce the emergence of the canonical frame, SDF predictions, or generalization performance to fitted parameters or self-referential definitions by construction. The approach is presented as a standard optimization-based learning procedure without load-bearing reductions to inputs, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The abstract does not specify numerical free parameters or new entities; the core modeling choice rests on the domain assumption that a canonical implicit field can be learned without poses.

axioms (1)
  • domain assumption Multi-view images without known poses can be aligned into a shared canonical coordinate system through learned implicit features.
    This premise underpins the entire implicit representation and continuous query mechanism described in the abstract.

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Reference graph

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