OpenNovelty.org

The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images without Any 3D Knowledge

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
novel view synthesisfeed-forwardscaling behavior
Abstract:

Recent advances in feed-forward Novel View Synthesis (NVS) have led to a divergence between two design philosophies: bias-driven methods, which rely on explicit 3D knowledge, such as handcrafted 3D representations (e.g., NeRF and 3DGS) and camera poses annotated by Structure-from-Motion algorithms, and data-centric methods, which learn to understand 3D structure implicitly from large-scale imagery data. This raises a fundamental question: which paradigm is more scalable in an era of ever-increasing data availability? In this work, we conduct a comprehensive analysis of existing methods and uncover a critical trend that the performance of methods requiring less 3D knowledge accelerates more as training data increases, eventually outperforming their 3D knowledge-driven counterparts, which we term “the less you depend, the more you learn.” Guided by this finding, we design a feed-forward NVS framework that removes both explicit scene structure and pose annotation reliance. By eliminating these dependencies, our method leverages great scalability, learning implicit 3D awareness directly from vast quantities of 2D images, without any pose information for training or inference. Extensive experiments demonstrate that our model achieves state-of-the-art NVS performance, even outperforming methods relying on posed training data. The results validate not only the effectiveness of our data-centric paradigm but also the power of our scalability finding as a guiding principle.

Disclaimer
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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a feed-forward novel view synthesis framework that eliminates reliance on explicit 3D representations and pose annotations, learning implicit 3D awareness from large-scale 2D imagery. It resides in the 'Pose-Free Learning from Data' leaf, which contains only three papers total, indicating a relatively sparse research direction within the broader taxonomy of 50 papers across 36 topics. This positioning suggests the work targets an emerging paradigm where geometric understanding is learned end-to-end rather than imposed through handcrafted priors or structure-from-motion pipelines.

The taxonomy reveals that most neighboring work falls under 'Joint Pose and Scene Optimization' or 'Pose Initialization and Refinement', which still engage with explicit camera parameters during training or inference. The broader 'Scene Representation and Reconstruction Approach' branch shows heavy activity in Gaussian splatting and NeRF methods, many of which assume known poses or iterative refinement. By contrast, the pose-free leaf explicitly excludes methods that estimate or optimize poses, clarifying that this work diverges from the dominant paradigm of geometric bootstrapping and instead pursues data-centric implicit learning.

Among 22 candidates examined, the UP-LVSM framework contribution shows one refutable candidate from 10 examined, suggesting some prior work addresses pose-free feed-forward synthesis. The systematic analysis contribution (10 candidates, zero refutations) and the Latent Plücker Learner (2 candidates, zero refutations) appear more novel within this limited search scope. The statistics indicate that while the core framework has identifiable precedent, the analytical insights and specific geometric inference mechanism may offer incremental advances over existing pose-free approaches, though the search scale precludes definitive claims about field-wide novelty.

Based on top-22 semantic matches, the work appears to occupy a sparsely populated niche where data-driven implicit geometry learning is prioritized over traditional pose estimation. The limited refutation count suggests moderate novelty, though the small candidate pool and narrow taxonomy leaf mean this assessment reflects local context rather than exhaustive field coverage. The analysis does not capture potential overlap with broader generative or self-supervised learning literatures outside the immediate NVS domain.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
22
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: novel view synthesis from sparse unposed images. This field addresses the challenge of generating photorealistic views of a scene when only a handful of input images are available and their camera poses are unknown or unreliable. The taxonomy reveals four main branches that capture different strategic emphases. Scene Representation and Reconstruction Approach focuses on how 3D structure is encoded—whether through neural radiance fields like RegNeRF[8], Gaussian splatting methods such as InstantSplat[13], or hybrid representations. Pose Estimation and Camera Modeling Strategy examines whether systems rely on explicit pose optimization, joint pose-and-scene learning as in GNeRF[25], or pose-free paradigms that bypass camera estimation entirely. Specialized Application Domains targets specific settings like dental imaging in DentalSplat[1] or human-centric scenarios. Single-View Novel View Synthesis explores the extreme case of generating multiple views from just one image, often leveraging strong priors from generative models like ZeroNVS[43] or UpFusion[15]. Recent work has intensified around pose-free learning strategies that avoid fragile pose estimation pipelines, particularly when input views are extremely sparse. Methods like No Pose No Problem[4] and Large Spatial Model[17] demonstrate that end-to-end learning can implicitly handle geometric relationships without explicit camera parameters. Less Depend More Learn[0] sits squarely within this pose-free paradigm, emphasizing data-driven approaches that reduce reliance on traditional geometric constraints. It contrasts with nearby efforts such as Edit3R[31], which integrates editing capabilities into pose-free reconstruction, highlighting a trade-off between pure synthesis fidelity and interactive manipulation. Meanwhile, works like NVComposer[3] explore compositional scene understanding under sparse unposed conditions, suggesting that the field is diversifying beyond monolithic reconstruction toward modular, interpretable representations that can handle complex real-world variability.

Claimed Contributions

Systematic analysis revealing that reducing 3D knowledge dependence unlocks scalability

The authors conduct a comprehensive analysis of existing feed-forward novel view synthesis methods, categorizing them by their reliance on explicit 3D knowledge (scene structure and pose annotations). They demonstrate empirically that methods requiring less 3D knowledge exhibit superior data scalability, with performance gains accelerating as training data increases, eventually surpassing methods that depend heavily on 3D priors.

10 retrieved papers
UP-LVSM framework for novel view synthesis without explicit 3D representations or pose supervision

The authors introduce UP-LVSM, a feed-forward novel view synthesis framework that eliminates dependencies on both explicit scene structure (such as NeRF or 3D Gaussian Splatting) and camera pose annotations. The method learns implicit 3D awareness directly from large-scale 2D image collections, operating in the challenging unposed setting where neither input nor target view poses are provided.

10 retrieved papers
Can Refute
Latent Plücker Learner for self-supervised camera geometry inference

The authors propose the Latent Plücker Learner, a novel component that learns camera pose representations in a self-supervised manner without pose annotations. It uses an autoencoder architecture with a compact 7D latent pose bottleneck that is then upsampled into fine-grained pixel-level conditions via Plücker ray embeddings, preventing information leakage while maintaining expressiveness for rendering.

2 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Systematic analysis revealing that reducing 3D knowledge dependence unlocks scalability

The authors conduct a comprehensive analysis of existing feed-forward novel view synthesis methods, categorizing them by their reliance on explicit 3D knowledge (scene structure and pose annotations). They demonstrate empirically that methods requiring less 3D knowledge exhibit superior data scalability, with performance gains accelerating as training data increases, eventually surpassing methods that depend heavily on 3D priors.

Contribution

UP-LVSM framework for novel view synthesis without explicit 3D representations or pose supervision

The authors introduce UP-LVSM, a feed-forward novel view synthesis framework that eliminates dependencies on both explicit scene structure (such as NeRF or 3D Gaussian Splatting) and camera pose annotations. The method learns implicit 3D awareness directly from large-scale 2D image collections, operating in the challenging unposed setting where neither input nor target view poses are provided.

Contribution

Latent Plücker Learner for self-supervised camera geometry inference

The authors propose the Latent Plücker Learner, a novel component that learns camera pose representations in a self-supervised manner without pose annotations. It uses an autoencoder architecture with a compact 7D latent pose bottleneck that is then upsampled into fine-grained pixel-level conditions via Plücker ray embeddings, preventing information leakage while maintaining expressiveness for rendering.

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