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EA3D: Event-Augmented 3D Diffusion for Generalizable Novel View Synthesis

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Novel view synthesis; Event Cameras; Diffusion model
Abstract:

We introduce EA3D, an Event-Augmented 3D Diffusion framework for generalizable novel view synthesis from event streams and sparse RGB inputs. Existing approaches either rely solely on RGB frames for generalizable synthesis, which limits their robustness under rapid camera motion, or require per-scene optimization to exploit event data, undermining scalability. EA3D addresses these limitations by jointly leveraging the complementary strengths of asynchronous events and RGB imagery. At its core lies a learnable EA-Renderer, which constructs view-dependent 3D features within target camera frustums by fusing appearance cues from RGB frames with geometric structure extracted from adaptively sliced event voxels. These features condition a 3D-aware diffusion model, enabling high-fidelity and temporally consistent novel view generation along arbitrary camera trajectories. To further enhance scalability and generalization, we develop the Event-DL3DV dataset, a large-scale 3D benchmark pairing diverse synthetic event streams with photorealistic multi-view RGB images and depth maps. Extensive experiments on both real-world and synthetic event data demonstrate that EA3D consistently outperforms optimization-based and generalizable baselines, achieving superior fidelity and cross-scene generalization.

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 EA3D, a diffusion-based framework that jointly leverages event streams and sparse RGB inputs for generalizable novel view synthesis. Within the taxonomy, it occupies the 'Generalizable Event-Augmented Diffusion Models' leaf, which currently contains only this work as a sibling. This positioning reflects a relatively sparse research direction: while the broader field includes fifteen papers across event-based 3D Gaussian splatting, neural radiance fields, and light field synthesis, the specific combination of diffusion priors with cross-scene generalization from mixed event-RGB modalities remains underexplored. The taxonomy structure reveals that most prior efforts concentrate on per-scene optimization or single-modality reconstruction, leaving this generalization-focused niche less crowded.

The taxonomy tree situates EA3D within a field organized around representation choices and reconstruction paradigms. Neighboring branches include Event-Based 3D Gaussian Splatting Methods, which prioritize explicit point-based rendering for efficiency, and Event-Based Neural Radiance Field Methods, which adopt implicit volumetric approaches for richer appearance modeling. Light Field Event Generation and Synthesis explores multi-view event data creation, while Neuromorphic Visual Representation Processing investigates biologically inspired encoding schemes. EA3D diverges from these directions by emphasizing learned diffusion priors that transfer across scenes without per-scene fitting, contrasting with methods like AE-NeRF or DiET-GS that optimize representations for individual captures. The taxonomy's scope and exclude notes clarify that diffusion-based generalization distinguishes this work from optimization-centric or single-modality approaches.

Among twenty-one candidates examined via limited semantic search, none clearly refute the three core contributions. The EA3D framework itself was assessed against ten candidates with zero refutable overlaps, suggesting that the specific integration of event-augmented rendering with diffusion-based generalization has minimal direct precedent in the examined literature. The Event-DL3DV dataset contribution also faced ten candidates without refutation, indicating that large-scale paired event-RGB-depth benchmarks remain scarce. The Event-Augmented Feature Renderer with adaptive slicing examined only one candidate, reflecting the narrow scope of this technical component. These statistics indicate that within the limited search horizon, the contributions appear relatively novel, though the small candidate pool and sparse taxonomy leaf suggest the analysis covers a focused subset of the broader event-based vision literature.

Taxonomy

Core-task Taxonomy Papers
15
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: generalizable novel view synthesis from event streams and sparse RGB inputs. The field has coalesced around several complementary directions. Event-Based 3D Gaussian Splatting Methods (e.g., E-3DGS[1], DiET-GS[5]) leverage explicit point-based scene representations for efficient rendering, while Event-Based Neural Radiance Field Methods (such as AE-NeRF[3], E2NeRF[9], Dynamic EventNeRF[7]) adopt implicit volumetric approaches that excel at modeling complex geometry and appearance. Light Field Event Generation and Synthesis explores multi-view event data creation (EV-LFV[2], S2D-LFE[4]), and Neuromorphic Visual Representation Processing investigates biologically inspired encoding schemes (SpikeGen[11], SpikeGen Rods Cones[12]). Event Camera Simulation and Data Generation provides synthetic training resources (Event Camera Simulator[14], Blur to Brilliance[13]), while Generalizable Event-Augmented Diffusion Models pursue learning-based priors that generalize across scenes without per-scene optimization. A central tension in the field is the trade-off between explicit geometric representations offering real-time performance and implicit neural methods providing richer appearance modeling. Many studies focus on single-scene reconstruction under controlled settings, whereas a smaller handful of works target cross-scene generalization with minimal input views. EA3D[0] sits within the Generalizable Event-Augmented Diffusion Models branch, emphasizing learned priors that transfer across diverse environments when only sparse RGB frames and event streams are available. This contrasts with approaches like AE-NeRF[3], which refines per-scene radiance fields using event supervision, and DiET-GS[5], which optimizes Gaussian splats for each capture. By leveraging diffusion-based generative modeling, EA3D[0] aims to bypass costly per-scene fitting, addressing scalability challenges that remain open questions for many event-driven reconstruction pipelines.

Claimed Contributions

EA3D framework for generalizable novel view synthesis from events and RGB

The authors introduce EA3D, a novel framework that combines event streams with sparse RGB frames to enable generalizable novel view synthesis without requiring per-scene optimization. The framework consists of an Event-Augmented Feature Renderer (EA-Renderer) that fuses appearance cues from RGB frames with geometric structure from event voxels, and a 3D-informed diffusion model for generating photorealistic novel views.

10 retrieved papers
Event-DL3DV dataset for large-scale training

The authors develop Event-DL3DV, a large-scale benchmark dataset that combines diverse synthetic event streams (with randomized contrast thresholds) with photorealistic multi-view RGB images and per-view depth maps from real-world sequences. This dataset supports large-scale training and encourages strong generalization ability of the model.

10 retrieved papers
Event-Augmented Feature Renderer with adaptive slicing

The authors design a learnable EA-Renderer that projects both appearance information from RGB frames and occlusion-resilient geometry from adaptively sliced event voxel grids into target camera frustums. The adaptive slicing strategy ensures sufficient voxel density under non-uniform event streams by adjusting time duration until required event counts are accumulated.

1 retrieved paper

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

EA3D framework for generalizable novel view synthesis from events and RGB

The authors introduce EA3D, a novel framework that combines event streams with sparse RGB frames to enable generalizable novel view synthesis without requiring per-scene optimization. The framework consists of an Event-Augmented Feature Renderer (EA-Renderer) that fuses appearance cues from RGB frames with geometric structure from event voxels, and a 3D-informed diffusion model for generating photorealistic novel views.

Contribution

Event-DL3DV dataset for large-scale training

The authors develop Event-DL3DV, a large-scale benchmark dataset that combines diverse synthetic event streams (with randomized contrast thresholds) with photorealistic multi-view RGB images and per-view depth maps from real-world sequences. This dataset supports large-scale training and encourages strong generalization ability of the model.

Contribution

Event-Augmented Feature Renderer with adaptive slicing

The authors design a learnable EA-Renderer that projects both appearance information from RGB frames and occlusion-resilient geometry from adaptively sliced event voxel grids into target camera frustums. The adaptive slicing strategy ensures sufficient voxel density under non-uniform event streams by adjusting time duration until required event counts are accumulated.

Table of Contents

EA3D: Event-Augmented 3D Diffusion for Generalizable Novel View Synthesis | Novelty Validation