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arxiv: 2605.30060 · v2 · pith:MGAGJO6Fnew · submitted 2026-05-28 · 💻 cs.CV

Towards Consistent Video Geometry Estimation

Zhu Yu , Jingnan Gao , Runmin Zhang , Lingteng Qiu , Zhengyi Zhao , Rui Peng , Yichao Yan , Kejie Qiu
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Siyu Zhu Zilong Dong Si-Yuan Cao Hui-Liang Shen
This is my paper

Pith reviewed 2026-06-29 08:00 UTC · model grok-4.3

classification 💻 cs.CV
keywords video geometry estimationdepth estimationsurface normal estimationpoint map estimationtransformer architecturetemporal consistencydata refinementfoundation model
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The pith

ViGeo recovers dense and temporally consistent video geometry with one plain transformer that adapts attention patterns at test time.

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

The paper introduces ViGeo as a feed-forward foundation model that produces spatially dense and temporally consistent geometry estimates, including depth, surface normals, and point maps, from video sequences. It achieves this with a plain transformer using dynamic chunking attention that trains on both bidirectional and causal contexts so the model can switch between streaming, full-sequence, and long-video inference without retraining. A completion-based data refinement framework trains a teacher model on sparse noisy labels to generate higher-quality dense targets by exploiting video context. A sympathetic reader would care because reliable consistent geometry from ordinary video is a prerequisite for stable 3D scene understanding in changing environments.

Core claim

ViGeo is a feed-forward foundation model for recovering spatially dense and temporally consistent geometry from video sequences. Built upon a plain transformer architecture without task-specific architectural modifications, ViGeo supports streaming, full-sequence, and long-video inference within a unified model. The key design is dynamic chunking attention, which exposes the model to both bidirectional and causal temporal contexts during training and allows it to adapt its attention pattern at test time without retraining. To improve supervision quality, the authors introduce a completion-based data refinement framework that trains a video depth completion teacher conditioning on sparse and

What carries the argument

Dynamic chunking attention, which exposes the model to both bidirectional and causal temporal contexts during training and allows adaptation of the attention pattern at test time without retraining.

If this is right

  • The same trained model can switch between online streaming depth estimation and offline bidirectional processing without retraining.
  • Surface normal and point map predictions are generated alongside depth within one forward pass.
  • Long-video sequences maintain geometric consistency using the adapted attention pattern.
  • Training targets refined by the teacher model improve supervision quality over raw annotations.
  • State-of-the-art results are obtained using only public datasets across the listed tasks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The unified attention mechanism could reduce the engineering overhead of maintaining separate models for different video lengths in production systems.
  • Consistent normal and point map outputs may directly feed into downstream tasks such as video-based 3D reconstruction or SLAM without additional alignment steps.
  • The refinement approach might generalize to other sparse supervision settings in video, such as optical flow or instance segmentation.
  • If the dynamic chunking pattern proves stable, similar attention designs could appear in other sequence models that must support both causal and non-causal inference.

Load-bearing premise

The completion-based data refinement framework produces dense, temporally coherent, and geometrically reliable training targets from sparse and noisy annotations.

What would settle it

If a model trained directly on the original sparse noisy annotations matches or exceeds ViGeo's temporal consistency scores on long-video benchmarks, the contribution of the refinement framework would be falsified.

Figures

Figures reproduced from arXiv: 2605.30060 by Hui-Liang Shen, Jingnan Gao, Kejie Qiu, Lingteng Qiu, Rui Peng, Runmin Zhang, Si-Yuan Cao, Siyu Zhu, Yichao Yan, Zhengyi Zhao, Zhu Yu, Zilong Dong.

Figure 1
Figure 1. Figure 1: ViGeo is a unified feed-forward foundation model for video geometry estimation. It predicts temporally consistent depth, surface normals, and dense point maps from raw video frames. With dynamic chunking attention, the same trained model seamlessly switches between full-sequence reconstruction and streaming inference without retraining. Abstract This work presents ViGeo, a feed-forward foundation model for… view at source ↗
Figure 2
Figure 2. Figure 2: Benchmark comparison with previous state-of-the-art methods. Video geometry estimation is a fundamental prob￾lem in computer vision, supporting applications such as robotics [38], augmented reality [61], au￾tonomous navigation [1], and video editing [11]. These applications require geometry that is both spatially accurate and temporally consistent over long video sequences. Despite recent progress, achievi… view at source ↗
Figure 3
Figure 3. Figure 3: Architecture overview of ViGeo. Built upon a plain Transformer with dynamic chunking attention, ViGeo supports full-sequence, streaming, and long-video inference within a unified model and predicts temporally consistent depth, surface normals, and point maps. models [88, 58], we devise a data engine based on multi-view depth completion, fully leveraging the strengths from both images and sparse measurement… view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of our completion-based data refinement pipeline. Given an RGB video [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on 3D reconstruction. Our method yields more accurate and robust [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Additional point cloud visualizations. ViGeo produces accurate and realistic reconstructions [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative results for video depth estimation. Compared with existing methods, ViGeo [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative results for monocular depth estimation. Compared with existing methods, [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative ablation of our data refinement pipeline. Our full pipeline effectively resolves [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative results of the data refinement pipeline. We visualize the sparse raw measure [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
read the original abstract

This work presents ViGeo, a feed-forward foundation model for recovering spatially dense and temporally consistent geometry from video sequences. Built upon a plain transformer architecture without task-specific architectural modifications, ViGeo supports streaming, full-sequence, and long-video inference within a unified model. The key design is dynamic chunking attention, which exposes the model to both bidirectional and causal temporal contexts during training and allows it to adapt its attention pattern at test time without retraining. To improve supervision quality, we further introduce a completion-based data refinement framework. This framework trains a video depth completion teacher that conditions on sparse and noisy annotations and exploits video/multi-view context to produce dense, temporally coherent, and geometrically reliable training targets. Beyond depth and point maps, ViGeo also predicts surface normals within the same framework. Trained solely on public datasets, ViGeo achieves state-of-the-art performance across online, offline, and long-video depth estimation, surface normal estimation, and video point map 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

1 major / 1 minor

Summary. The paper presents ViGeo, a feed-forward foundation model based on a plain transformer for recovering spatially dense and temporally consistent geometry (depth, surface normals, point maps) from video. It introduces dynamic chunking attention to enable streaming, full-sequence, and long-video inference within one model. A completion-based data refinement framework trains a video depth completion teacher on sparse/noisy annotations to generate dense, temporally coherent training targets. Trained only on public datasets, ViGeo claims state-of-the-art results on online/offline/long-video depth estimation, surface normal estimation, and video point map estimation.

Significance. If substantiated, the work would advance video geometry estimation by offering a unified, architecture-agnostic foundation model with flexible inference modes. The dynamic chunking attention and public-data training are positive elements that could support broader adoption if the performance claims are rigorously validated.

major comments (1)
  1. [Data refinement framework] Data refinement framework (abstract and methods section): The SOTA claims depend on the teacher producing 'geometrically reliable' dense targets. No quantitative validation against independent dense ground truth is described, nor are details given on the teacher's loss beyond sparse-point conditioning or explicit multi-view consistency terms. This is load-bearing because unverified target quality could mean reported metric gains reflect annotation propagation rather than model capability.
minor comments (1)
  1. [Abstract] The abstract states the model 'exploits video/multi-view context' in the teacher but provides no implementation specifics or ablation on this component.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thoughtful feedback on our data refinement framework. We address the concern point-by-point below and will incorporate additional details and validation in the revised manuscript to strengthen the presentation of the teacher model's target quality.

read point-by-point responses

  1. Referee: [Data refinement framework] Data refinement framework (abstract and methods section): The SOTA claims depend on the teacher producing 'geometrically reliable' dense targets. No quantitative validation against independent dense ground truth is described, nor are details given on the teacher's loss beyond sparse-point conditioning or explicit multi-view consistency terms. This is load-bearing because unverified target quality could mean reported metric gains reflect annotation propagation rather than model capability.

    Authors: We agree that the current manuscript provides insufficient quantitative validation of the teacher's dense outputs against independent dense ground truth and limited specifics on the full loss formulation. While the framework description emphasizes conditioning on sparse/noisy annotations and exploitation of video/multi-view context to generate coherent targets, we acknowledge this leaves open the possibility that gains partly reflect propagation of existing annotations. In the revision we will expand the methods section with: (1) the complete teacher loss, explicitly including any multi-view consistency or geometric regularization terms beyond sparse-point conditioning; (2) quantitative evaluation of teacher outputs on any available dense ground-truth subsets (e.g., selected sequences from datasets that provide both sparse and dense annotations); and (3) an ablation isolating the effect of the refined targets versus raw sparse supervision. These additions will allow readers to better assess whether the reported improvements stem from model capability or target quality. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on external benchmarks

full rationale

The provided abstract and description introduce ViGeo and a completion-based data refinement framework that generates training targets from public datasets' sparse annotations. Performance claims are evaluated on standard external metrics for depth, normals, and point maps across online/offline/long-video settings. No equations, self-citations, or derivations are shown that reduce any prediction or result to its own inputs by construction. The framework is presented as a training aid rather than a self-referential definition of success, and no uniqueness theorems or ansatzes are invoked via self-citation. The derivation chain is self-contained against public data and independent benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no information on free parameters, axioms, or invented entities.

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discussion (0)

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