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Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
unified model; generation helps understanding; 3d scene understanding; novel view synthesis
Abstract:

This paper presents Omni-View, which extends the unified multimodal understanding and generation to 3D scenes based on multiview images, exploring the principle that ``generation facilitates understanding". Consisting of understanding model, texture module, and geometry module, Omni-View jointly models scene understanding, novel view synthesis, and geometry estimation, enabling synergistic interaction between 3D scene understanding and generation tasks. By design, it leverages the spatiotemporal modeling capabilities of its texture module responsible for appearance synthesis, alongside the explicit geometric constraints provided by its dedicated geometry module, thereby enriching the model’s holistic understanding of 3D scenes. Trained with a two-stage strategy, Omni-View achieves a state-of-the-art score of 55.4 on the VSI-Bench benchmark, outperforming existing specialized 3D understanding models, while simultaneously delivering strong performance in both novel view synthesis and 3D scene generation.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

Omni-View proposes a unified framework for 3D scene understanding and generation from multiview images, integrating an understanding model with texture and geometry modules. The paper resides in the 'Unified Reconstruction and Semantic Understanding' leaf, which contains only two papers total, indicating a relatively sparse research direction within the broader Gaussian splatting-based feed-forward methods. This positioning suggests the work targets an emerging intersection of semantic interpretation and geometric reconstruction, rather than a densely populated subfield.

The taxonomy reveals that Omni-View's leaf sits within 'Gaussian Splatting-Based Feed-Forward Methods', which includes neighboring leaves focused on cost volume guidance, hierarchical representations, and diffusion-based probabilistic reconstruction. The sibling paper in the same leaf (Uni3r) also addresses unified semantic-geometric modeling, suggesting direct competition in this specific niche. Broader neighboring branches include optimization-based methods for dynamic scenes and specialized domain reconstruction, which diverge by requiring per-scene refinement or targeting narrow application contexts rather than generalizable feed-forward inference.

Among the three contributions analyzed from 30 candidate papers examined, the unified model architecture shows no clear refutation across 10 candidates, suggesting potential novelty in the overall framework design. The dual-module generation architecture encountered one refutable candidate among 10 examined, indicating some prior work on separating texture and geometry synthesis pathways. The two-stage training strategy appears more novel, with zero refutable candidates among 10 examined. These statistics reflect a limited search scope and suggest the architectural integration may be more incremental than the training methodology.

Based on the top-30 semantic matches examined, Omni-View appears to occupy a sparsely populated research direction with limited direct competition. The analysis does not cover exhaustive prior work in related fields like NeRF-based unified models or optimization-based semantic reconstruction, which may contain relevant overlapping ideas. The contribution-level findings suggest moderate novelty in framework design and training strategy, though the dual-module architecture shows some precedent in the examined literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: unified 3D scene understanding and generation from multiview images. The field has evolved into several major branches that reflect different methodological philosophies and application contexts. Feed-forward generalizable reconstruction methods, exemplified by Gaussian splatting-based approaches like Mvsplat[1] and Uni3r[2], prioritize rapid inference by learning priors from large datasets, enabling single-pass reconstruction without per-scene optimization. In contrast, optimization-based novel view synthesis techniques iteratively refine scene representations, often achieving higher fidelity at the cost of computational expense. Specialized domain reconstruction addresses unique challenges in medical imaging, underwater environments, and aerial photography, while autonomous driving and urban scene applications like Urban Gaussian Splatting[5] tackle large-scale outdoor scenarios with dynamic objects. Sparse-view and few-view reconstruction methods such as SparSplat[11] focus on data-efficient settings, and scene editing branches explore post-reconstruction manipulation capabilities. Foundational approaches and datasets provide the theoretical and empirical bedrock for these diverse directions. Recent work has intensified around feed-forward Gaussian splatting methods that unify geometric reconstruction with semantic understanding, balancing speed and quality trade-offs. Omni-View[0] sits squarely within this active cluster, emphasizing unified reconstruction and semantic understanding through Gaussian splatting-based feed-forward architectures. Compared to Uni3r[2], which also targets unified semantic-geometric modeling, Omni-View[0] appears to push further on multiview consistency and generalization across diverse scene types. Meanwhile, methods like Dynamic 3D Gaussians[6] and E-4DGS[18] extend these ideas to dynamic scenes, and works such as HERMES[12] explore hierarchical representations. The central tension across these branches remains between generalization breadth—handling arbitrary scenes with minimal input—and reconstruction fidelity, with ongoing questions about how best to incorporate semantic priors, handle sparse observations, and scale to complex real-world environments without sacrificing real-time performance.

Claimed Contributions

Omni-View unified 3D understanding and generation model

The authors introduce Omni-View, a unified model that jointly performs 3D scene understanding, novel view synthesis, and geometry estimation from multiview images. The model demonstrates that generative tasks can enhance understanding capabilities in the 3D domain.

10 retrieved papers
Dual-module generation architecture with texture and geometry modules

The generation component is decomposed into separate texture and geometry modules. The texture module handles novel view synthesis while the geometry module estimates depth maps and camera poses, enabling the model to develop both geometric and spatiotemporal modeling capabilities.

10 retrieved papers
Can Refute
Two-stage training strategy with dense-to-sparse curriculum

The authors propose a two-stage training approach where stage 1 jointly trains understanding, texture, and geometry modules with a dense-to-sparse reference image curriculum, while stage 2 refines generation through RGB-Depth-Pose joint learning.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Omni-View unified 3D understanding and generation model

The authors introduce Omni-View, a unified model that jointly performs 3D scene understanding, novel view synthesis, and geometry estimation from multiview images. The model demonstrates that generative tasks can enhance understanding capabilities in the 3D domain.

Contribution

Dual-module generation architecture with texture and geometry modules

The generation component is decomposed into separate texture and geometry modules. The texture module handles novel view synthesis while the geometry module estimates depth maps and camera poses, enabling the model to develop both geometric and spatiotemporal modeling capabilities.

Contribution

Two-stage training strategy with dense-to-sparse curriculum

The authors propose a two-stage training approach where stage 1 jointly trains understanding, texture, and geometry modules with a dense-to-sparse reference image curriculum, while stage 2 refines generation through RGB-Depth-Pose joint learning.

Table of Contents