Weaving Light and Time: Unified Harmonic-Geometric Representation Learning for Dense RGB-Event Parsing

• URL: http://arxiv.org/abs/2607.09143v1
• Date: Fri, 10 Jul 2026 06:52:09 GMT
• Title: Weaving Light and Time: Unified Harmonic-Geometric Representation Learning for Dense RGB-Event Parsing
• Abstract summary: We present Evita, the first unified backbone specifically engineered for dedicated dense RGB-Event parsing.<n>To achieve profound modal synergy, Evita embeds a suite of intrinsic co-learning modules directly into every encoder layer.<n>Evita establishes new state-of-the-art metrics while delivering a superior accuracy-latency trade-off for real-time multimodal perception.
• Score: 55.13802890769086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fusing standard RGB frames with asynchronous event streams has emerged as a definitive paradigm for robust perception in degraded environments. Although unified backbones have recently gained traction in multi-modal vision, adapting them to the RGB-Event domain remains fundamentally challenging. Existing architectures either resort to decoupled dual encoders that double computational overhead, or adopt generic unified designs that fail to resolve implicit geometric parallax and cross-spectral aliasing under the extreme representational divide between dense intensity grids and sparse kinematic spikes. To transcend these bottlenecks, we present Evita, the first unified backbone specifically engineered for dedicated dense RGB-Event parsing. To achieve profound modal synergy, Evita explicitly embeds a suite of intrinsic co-learning modules directly into every encoder layer. Specifically, it features Geometric Parallax Rectification for adaptive spatial alignment, Harmonic Spectral Resonance for texture transfer exclusively in the complex frequency domain, and Transient Global Routing for event-driven asymmetric attention. To guarantee robust feature extraction against spatial misalignments and decouple representations from specific event encodings, we construct N-ImageNetV2 alongside a stochastic event representation mixing pretraining protocol, empowering the network to seamlessly accommodate arbitrary event formats in downstream tasks. Extensive evaluations across the DELIVER, DDD17, and DSEC benchmarks confirm that Evita establishes new state-of-the-art metrics while delivering a superior accuracy-latency trade-off for real-time multimodal perception.The code are publicly available at: https://github.com/chaineypung/Evita.

Related papers

• Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking [58.338848435042536]
  RGB-Event tracking improves localization by fusing RGB appearance textures and dense temporal motion cues from event sensors.<n>Real-world scenes suffer diverse structured signal degradations that hinder traditional multi-modal fusion.<n>We present a hierarchical perturbation and retrieval framework tailored for RGB-Event tracking with robustness against partial target missing and modal degradation.
  arXiv  Detail & Related papers  (2026-06-24T23:34:06Z)
• Spatial Orthogonal Refinement for Robust RGB-Event Visual Object Tracking [8.019596736149834]
  We propose SOR-Track, a framework for robust RGB-Event tracking based on Spatial Orthogonal Refinement (SOR)<n>Experiments on the large-scale FE108 benchmark demonstrate that SOR-Track consistently outperforms existing fusion-based trackers.<n>Despite its simplicity, the proposed method offers a principled and physics-grounded approach to multi-modal feature alignment and texture rectification.
  arXiv  Detail & Related papers  (2026-03-29T23:54:31Z)
• RTFDNet: Fusion-Decoupling for Robust RGB-T Segmentation [0.2578242050187029]
  RGB-Thermal (RGB-T) semantic segmentation is essential for robotic systems operating in low-light environments.<n>Traditional approaches often overemphasize modality balance, resulting in limited robustness or severe performance when sensor signals are partially missing.
  arXiv  Detail & Related papers  (2026-03-10T03:40:26Z)
• PEPR: Privileged Event-based Predictive Regularization for Domain Generalization [19.185122873391517]
  We propose a cross-modal framework under the learning using privileged information (LUPI) paradigm for training a robust, single-modality RGB model.<n>We leverage event cameras as a source of privileged information, available only during training.<n>We train the RGB encoder with PEPR to predict event-based latent features, distilling robustness without sacrificing semantic richness.
  arXiv  Detail & Related papers  (2026-02-04T14:10:36Z)
• Decoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking [51.31378940976401]
  Existing RGB-Event tracking approaches fail to fully exploit the unique advantages of event cameras.<n>We propose a novel tracking framework that performs early fusion in the frequency domain, enabling effective aggregation of high-frequency information from the event modality.<n>Experiments on three widely used RGB-Event tracking benchmark datasets, including FE108, FELT, and COESOT, demonstrate the high performance and efficiency of our method.
  arXiv  Detail & Related papers  (2026-01-03T01:10:17Z)
• Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
  We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
  arXiv  Detail & Related papers  (2025-09-18T03:51:06Z)
• Learning Flow-Guided Registration for RGB-Event Semantic Segmentation [22.996619370156584]
  Event cameras capture microsecond-level motion cues that complement RGB sensors.<n>We recast RGB-Event segmentation from fusion to registration.<n>We propose BRENet, a novel flow-guided bidirectional framework that adaptively matches correspondence between the asymmetric modalities.
  arXiv  Detail & Related papers  (2025-05-02T19:19:58Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [92.4205087439928]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose the Self-supervised Transfer (PST) and the FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models, effectively mitigating data scarcity.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.<n>This combined approach enables FUSE to construct a universal image-event that only requires lightweight decoder adaptation for target datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.