OpenNovelty.org

RoRE: Rotary Ray Embedding for Generalised Multi-Modal Scene Understanding

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
transformersattentiongeometric visionmulti-modal visionnovel view synthesisthermalfisheye
Abstract:

Transformers have emerged as powerful implicit rendering models, capable of performing geometric reasoning and producing photorealistic novel views in a single feedforward pass. A central challenge in these architectures is how to inject camera parameters into the transformer in a way that generalises across diverse sensing conditions. In this work, we present Rotary Ray Embedding (RoRE), an approach that embeds image patches directly as rays, using a learning based rotary positional embedding (RoPE). This ray-based formulation provides a unified and general representation, improving robustness to unconventional camera geometries and sensing modalities. We evaluate our approach on conventional perspective imagery, fisheye cameras, and multi-modal RGB-thermal setups, showing that a single network can flexibly integrate arbitrary numbers of cameras and modalities into a coherent scene representation. Experiments demonstrate improved generalisation and cross-modal consistency compared to existing methods, highlighting the potential for relative ray-based embeddings to build adaptable, plug-and-play vision systems.

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 introduces Rotary Ray Embedding (RoRE), a method for embedding image patches as rays using rotary positional encoding to improve transformer-based implicit rendering across diverse camera geometries and sensing modalities. According to the taxonomy, this work resides in the 'Multi-Modal Scene Understanding and Rendering' leaf under 'Specialized Generalization Methods and Applications'. Notably, this leaf contains only the original paper itself, with no sibling papers identified, suggesting this represents a relatively sparse and specialized research direction within the broader field of multi-task and multi-domain generalization.

The taxonomy reveals that neighboring research directions include vision-specific generalization methods (person re-identification, depth completion), multi-modal alignment and fusion techniques, and general domain generalization approaches. The scope note for the original paper's leaf explicitly excludes multi-modal alignment without rendering and single-modality vision tasks, positioning RoRE at the intersection of geometric reasoning, multi-modal integration, and novel view synthesis. This boundary placement distinguishes the work from broader multi-modal fusion methods that do not address camera geometry or rendering, and from single-modality vision approaches that lack cross-modal consistency requirements.

Among the three identified contributions, the literature search examined twenty-three candidates total. For the core Rotary Ray Embedding contribution, ten candidates were examined with zero refutable overlaps found. The multi-modal training scheme examined four candidates with no refutations, and the MultiModalBlender dataset examined nine candidates, also with no refutations. These statistics suggest that within the limited scope of top-K semantic search plus citation expansion, no prior work was identified that directly anticipates or overlaps with the proposed ray-based rotary embedding approach or the specific multi-modal rendering framework presented.

Based on the limited search scope of twenty-three candidates, the work appears to occupy a novel position combining rotary positional embeddings with ray-based scene representations for multi-modal rendering. The absence of sibling papers in the taxonomy leaf and zero refutable candidates across all contributions suggests relative novelty within the examined literature. However, this assessment is constrained by the search methodology and does not constitute an exhaustive survey of all potentially relevant prior work in neural rendering, positional encoding, or multi-modal vision systems.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Generalising multi. The field of multi-task and multi-domain generalization encompasses a diverse set of approaches aimed at building models that perform well across varied tasks, domains, and modalities. The taxonomy reveals six major branches: Multi-Task Learning Optimization and Architectures focuses on parameter sharing and gradient balancing strategies (e.g., Pareto Multi-Task[28], Scalarization at Scale[22]); Domain Generalization and Cross-Domain Transfer addresses distribution shift and style variation (e.g., Meta-Learning Domain Generalization[5], MixStyle[11]); Cross-Task Generalization and Instruction Following emphasizes compositional reasoning and zero-shot transfer; Multi-Modal and Cross-Modal Learning integrates vision, language, and other sensory inputs; Robotics and Embodied AI Generalization tackles physical interaction and long-horizon planning (e.g., Bridge Data[9], Long-Horizon Generalization[16]); and Specialized Generalization Methods and Applications covers domain-specific techniques ranging from medical imaging to industrial diagnostics and scene understanding. Recent work highlights tensions between task-specific tuning and broad generalization, with many studies exploring how to balance conflicting objectives or leverage meta-learning for rapid adaptation. Within the Specialized Generalization Methods and Applications branch, a small cluster of papers addresses multi-modal scene understanding and rendering, where the challenge is to synthesize coherent 3D representations from diverse viewpoints and sensor modalities. Rotary Ray Embedding[0] sits within this cluster, proposing a novel encoding scheme for ray-based rendering that aims to improve generalization across viewpoints and scene configurations. Compared to broader multi-task frameworks like Multi-Task Multi-Objective[1] or domain adaptation methods such as Meta-Learning Domain Generalization[5], Rotary Ray Embedding[0] focuses on a more specialized geometric inductive bias tailored to neural rendering, reflecting the branch's emphasis on domain-specific architectural innovations rather than general-purpose optimization strategies.

Claimed Contributions

Rotary Ray Embedding (RoRE)

The authors propose RoRE, a novel positional embedding method that represents image patches as rays using learned rotation frequencies and asymmetric rotations. This ray-based formulation extends RoPE to handle diverse camera geometries and sensing modalities in a unified framework.

10 retrieved papers
Multi-modal training scheme with modality-specific tokenisers

The authors develop a training approach that uses separate tokenisers for different modalities (RGB, thermal, depth) while sharing the same ray-based positional embeddings. The method employs masked cross-modality prediction to enable robust multi-modal fusion without requiring confocal images.

4 retrieved papers
MultiModalBlender synthetic dataset

The authors introduce a new synthetic dataset containing 4,000 indoor scenes with RGB, thermal, and depth images along with ground-truth camera poses. This dataset addresses the scarcity of large-scale multi-modal data needed for training transformer-based vision models.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Rotary Ray Embedding (RoRE)

The authors propose RoRE, a novel positional embedding method that represents image patches as rays using learned rotation frequencies and asymmetric rotations. This ray-based formulation extends RoPE to handle diverse camera geometries and sensing modalities in a unified framework.

Contribution

Multi-modal training scheme with modality-specific tokenisers

The authors develop a training approach that uses separate tokenisers for different modalities (RGB, thermal, depth) while sharing the same ray-based positional embeddings. The method employs masked cross-modality prediction to enable robust multi-modal fusion without requiring confocal images.

Contribution

MultiModalBlender synthetic dataset

The authors introduce a new synthetic dataset containing 4,000 indoor scenes with RGB, thermal, and depth images along with ground-truth camera poses. This dataset addresses the scarcity of large-scale multi-modal data needed for training transformer-based vision models.

Table of Contents