Semantic-E2VID: a Semantic-Enriched Paradigm for Event-to-Video Reconstruction
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| Format: | Preprint |
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2025
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| _version_ | 1866912807506673664 |
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| author | Wu, Jingqian Jia, Yunbo Xu, Shengpeng Lam, Edmund Y. |
| author_facet | Wu, Jingqian Jia, Yunbo Xu, Shengpeng Lam, Edmund Y. |
| contents | Event cameras provide a promising sensing modality for high-speed and high-dynamic-range vision by asynchronously capturing brightness changes. A fundamental task in event-based vision is event-to-video (E2V) reconstruction, which aims to recover intensity videos from event streams. Most existing E2V approaches formulate reconstruction as a temporal--spatial signal recovery problem, relying on temporal aggregation and spatial feature learning to infer intensity frames. While effective to some extent, this formulation overlooks a critical limitation of event data: due to the change-driven sensing mechanism, event streams are inherently semantically under-determined, lacking object-level structure and contextual information that are essential for faithful reconstruction. In this work, we revisit E2V from a semantic perspective and argue that effective reconstruction requires going beyond temporal and spatial modeling to explicitly account for missing semantic information. Based on this insight, we propose \textit{Semantic-E2VID}, a semantic-enriched end-to-end E2V framework that reformulates reconstruction as a process of semantic learning, fusing and decoding. Our approach first performs semantic abstraction by bridging event representations with semantics extracted from a pretrained Segment Anything Model (SAM), while avoiding modality-induced feature drift. The learned semantics are then fused into the event latent space in a representation-compatible manner, enabling event features to capture object-level structure and contextual cues. Furthermore, semantic-aware supervision is introduced to explicitly guide the reconstruction process toward semantically meaningful regions, complementing conventional pixel-level and temporal objectives. Extensive experiments on six public benchmarks demonstrate that Semantic-E2VID consistently outperforms state-of-the-art E2V methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_17347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-E2VID: a Semantic-Enriched Paradigm for Event-to-Video Reconstruction Wu, Jingqian Jia, Yunbo Xu, Shengpeng Lam, Edmund Y. Computer Vision and Pattern Recognition Event cameras provide a promising sensing modality for high-speed and high-dynamic-range vision by asynchronously capturing brightness changes. A fundamental task in event-based vision is event-to-video (E2V) reconstruction, which aims to recover intensity videos from event streams. Most existing E2V approaches formulate reconstruction as a temporal--spatial signal recovery problem, relying on temporal aggregation and spatial feature learning to infer intensity frames. While effective to some extent, this formulation overlooks a critical limitation of event data: due to the change-driven sensing mechanism, event streams are inherently semantically under-determined, lacking object-level structure and contextual information that are essential for faithful reconstruction. In this work, we revisit E2V from a semantic perspective and argue that effective reconstruction requires going beyond temporal and spatial modeling to explicitly account for missing semantic information. Based on this insight, we propose \textit{Semantic-E2VID}, a semantic-enriched end-to-end E2V framework that reformulates reconstruction as a process of semantic learning, fusing and decoding. Our approach first performs semantic abstraction by bridging event representations with semantics extracted from a pretrained Segment Anything Model (SAM), while avoiding modality-induced feature drift. The learned semantics are then fused into the event latent space in a representation-compatible manner, enabling event features to capture object-level structure and contextual cues. Furthermore, semantic-aware supervision is introduced to explicitly guide the reconstruction process toward semantically meaningful regions, complementing conventional pixel-level and temporal objectives. Extensive experiments on six public benchmarks demonstrate that Semantic-E2VID consistently outperforms state-of-the-art E2V methods. |
| title | Semantic-E2VID: a Semantic-Enriched Paradigm for Event-to-Video Reconstruction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.17347 |