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Kanghao Chen

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

EvDiff3D: Event-Aware Diffusion Repair for High-Fidelity Event-Based 3D Reconstruction

Kanghao Chen
Zixin Zhang
Hangyu Li
Lin Wang
Zeyu Wang

Event cameras are bio-inspired sensors that capture visual information through asynchronous brightness changes, offering distinct advantages including high temporal resolution and wide dynamic range. While prior research has investigated event-based 3D reconstruction for extreme scenarios, existing methods face inherent limitations and fail to fully exploit the unique characteristics of event data. In this paper, we present EvDiff3D, a novel two-stage 3D reconstruction framework that integrates event-based geometric constraints with an event-aware diffusion prior for appearance refinement. Our key insight lies in bridging the gap between physically grounded event-based reconstruction and data-driven appearance repair through a unified cyclical pipeline. In the first stage, we reconstruct a coarse 3D scene under supervision from event loss and event-based monocular depth constraints to preserve structural fidelity. The second stage fine-tunes an event-aware diffusion model based on a pretrained video diffusion model as a repair prior to enhance the appearance in under-constrained regions. Based on the diffusion model, our pipeline operates within a reconstruction-generation cycle that progressively refines both geometry and appearance using only event data. Extensive experiments on synthetic and real-world datasets demonstrate that EvDiff3D significantly outperforms existing methods in perceptual quality and structural consistency.

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AAAI Conference 2026 Conference Paper

T-Rex-Omni: Integrating Negative Visual Prompt in Generic Object Detection

Jiazhou Zhou
Qing Jiang
Kanghao Chen
Lutao Jiang
Yuanhuiyi Lyu
Ying-Cong Chen
Lei Zhang

Object detection methods have evolved from closed-set to open-set paradigms over the years. Current open-set object detectors, however, remain constrained by their exclusive reliance on positive indicators based on given prompts like text descriptions or visual exemplars. This positive-only paradigm experiences consistent vulnerability to visually similar but semantically different distractors. We propose T-Rex-Omni, a novel framework that addresses this limitation by incorporating negative visual prompts to negate hard negative distractors. Specifically, we first introduce a unified visual prompt encoder that jointly processes positive and negative visual prompts. Next, a training-free Negating Negative Computing (NNC) module is proposed to dynamically suppress negative responses during the probability computing stage. To further boost performance through fine-tuning, our Negating Negative Hinge (NNH) loss enforces discriminative margins between positive and negative embeddings. T-Rex-Omni supports flexible deployment in both positive-only and joint positive-negative inference modes, accommodating either user-specified or automatically generated negative examples. Extensive experiments demonstrate remarkable zero-shot detection performance, significantly narrowing the performance gap between visual-prompted and text-prompted methods while showing particular strength in long-tailed scenarios (51.2 AP_r on LVIS-minival). This work establishes negative prompts as a crucial new dimension for advancing open-set visual recognition systems.

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ICRA Conference 2025 Conference Paper

Elite-EvGS: Learning Event-based 3D Gaussian Splatting by Distilling Event-to-Video Priors

Zixin Zhang
Kanghao Chen
Lin Wang 0025

Event cameras are bio-inspired sensors that output asynchronous and sparse event streams, instead of fixed frames. Benefiting from their distinct advantages, such as high dynamic range and high temporal resolution, event cameras have been applied to address 3D reconstruction, important for robotic mapping. Recently, neural rendering techniques, such as 3D Gaussian splatting (3DGS), have been shown successful in 3D reconstruction. However, it still remains under-explored how to develop an effective event-based 3DGS pipeline. In particular, as 3DGS typically depends on high-quality initialization and dense multiview constraints, a potential problem appears for the 3DGS optimization with events given its inherent sparse property. To this end, we propose a novel event-based 3DGS framework, named Elite-EvGS. Our key idea is to distill the prior knowledge from the off-the-shelf event-to-video (E2V) models to effectively reconstruct 3D scenes from events in a coarse-to-fine optimization manner. Specifically, to address the complexity of 3DGS initialization from events, we introduce a novel warm-up initialization strategy that optimizes a coarse 3DGS from the frames generated by E2V models and then incorporates events to refine the details. Then, we propose a progressive event supervision strategy that employs the window-slicing operation to progressively reduce the number of events used for supervision. This subtly relives the temporal randomness of the event frames, benefiting the optimization of local textural and global structural details. Experiments on the benchmark datasets demonstrate that Elite-EvGS can reconstruct 3D scenes with better textural and structural details. Meanwhile, our method yields plausible performance on the captured real-world data, including diverse challenging conditions, such as fast motion and low light scenes. For demo and more results, please check our project page.

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NeurIPS Conference 2025 Conference Paper

Event-Guided Consistent Video Enhancement with Modality-Adaptive Diffusion Pipeline

Kanghao Chen
Zixin Zhang
Guoqiang Liang
Lutao Jiang
Zeyu Wang
Yingcong Chen

Recent advancements in low-light video enhancement (LLVE) have increasingly leveraged both RGB and event cameras to improve video quality under challenging conditions. However, existing approaches share two key drawbacks. First, they are tuned for steady low-light scenes, so their performance drops when illumination varies. Second, they assume every sensing modality is always available, while real systems may lose or corrupt one of them. These limitations make the methods brittle in dynamic, real-world settings. In this paper, we propose EVDiffuser, a novel framework for consistent LLVE that integrates RGB and event data through a modality-adaptive diffusion pipeline. By harnessing the powerful priors of video diffusion models, EVDiffuser enables consistent video enhancement and generalization to diverse scenarios under varying illumination, where RGB or events may even be absent. Specifically, we first design a modality-agnostic conditioning mechanism based on a diffusion pipeline by treating the two modalities as optional conditions, which is fine-tuned using augmented and integrated datasets. Furthermore, we introduce a modality-adaptive guidance rescaling that dynamically adjusts the contribution of each modality according to sensor-specific characteristics. Additionally, we establish a benchmark that accounts for varying illumination and diverse real-world scenarios, facilitating future research on consistent event-guided LLVE. Our experiments demonstrate state-of-the-art performance across challenging scenarios (i. e. , varying illumination) and sensor-based settings (e. g. , event-only, RGB-only), highlighting the generalization of our framework.

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NeurIPS Conference 2025 Conference Paper

PASS: Path-selective State Space Model for Event-based Recognition

Jiazhou Zhou
Kanghao Chen
Lei Zhang
Lin Wang

Event cameras are bio-inspired sensors that capture intensity changes asynchronously with distinct advantages, such as high temporal resolution. Existing methods for event-based object/action recognition predominantly sample and convert event representation at every fixed temporal interval (or frequency). However, they are constrained to processing a limited number of event lengths and show poor frequency generalization, thus not fully leveraging the event's high temporal resolution. In this paper, we present our PASS framework, exhibiting superior capacity for spatiotemporal event modeling towards a larger number of event lengths and generalization across varying inference temporal frequencies. Our key insight is to learn adaptively encoded event features via the state space models (SSMs), whose linear complexity and generalization on input frequency make them ideal for processing high temporal resolution events. Specifically, we propose a Path-selective Event Aggregation and Scan (PEAS) module to encode events into features with fixed dimensions by adaptively scanning and selecting aggregated event presentation. On top of it, we introduce a novel Multi-faceted Selection Guiding (MSG) loss to minimize the randomness and redundancy of the encoded features during the PEAS selection process. Our method outperforms prior methods on five public datasets and shows strong generalization across varying inference frequencies with less accuracy drop (ours -8. 62% v. s. -20. 69% for the baseline). Moreover, our model exhibits strong long spatiotemporal modeling for a broader distribution of event length (1-10^9), precise temporal perception, and effective generalization for real-world scenarios. Code and checkpoints will be released upon acceptance.

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NeurIPS Conference 2024 Conference Paper

LaSe-E2V: Towards Language-guided Semantic-aware Event-to-Video Reconstruction

Kanghao Chen
Hangyu Li
Jiazhou Zhou
Zeyu Wang
Lin Wang

Event cameras harness advantages such as low latency, high temporal resolution, and high dynamic range (HDR), compared to standard cameras. Due to the distinct imaging paradigm shift, a dominant line of research focuses on event-to-video (E2V) reconstruction to bridge event-based and standard computer vision. However, this task remains challenging due to its inherently ill-posed nature: event cameras only detect the edge and motion information locally. Consequently, the reconstructed videos are often plagued by artifacts and regional blur, primarily caused by the ambiguous semantics of event data. In this paper, we find language naturally conveys abundant semantic information, rendering it stunningly superior in ensuring semantic consistency for E2V reconstruction. Accordingly, we propose a novel framework, called LaSe-E2V, that can achieve semantic-aware high-quality E2V reconstruction from a language-guided perspective, buttressed by the text-conditional diffusion models. However, due to diffusion models' inherent diversity and randomness, it is hardly possible to directly apply them to achieve spatial and temporal consistency for E2V reconstruction. Thus, we first propose an Event-guided Spatiotemporal Attention (ESA) module to condition the event data to the denoising pipeline effectively. We then introduce an event-aware mask loss to ensure temporal coherence and a noise initialization strategy to enhance spatial consistency. Given the absence of event-text-video paired data, we aggregate existing E2V datasets and generate textual descriptions using the tagging models for training and evaluation. Extensive experiments on three datasets covering diverse challenging scenarios (e. g. , fast motion, low light) demonstrate the superiority of our method. Demo videos for the results are attached to the project page.

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JBHI Journal 2023 Journal Article

PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification

Kanghao Chen
Weixian Lei
Shen Zhao
Wei-Shi Zheng
Ruixuan Wang

Imbalanced training data in medical image diagnosis is a significant challenge for diagnosing rare diseases. For this purpose, we propose a novel two-stage Progressive Class-Center Triplet (PCCT) framework to overcome the class imbalance issue. In the first stage, PCCT designs a class-balanced triplet loss to coarsely separate distributions of different classes. Triplets are sampled equally for each class at each training iteration, which alleviates the imbalanced data issue and lays solid foundation for the successive stage. In the second stage, PCCT further designs a class-center involved triplet strategy to enable a more compact distribution for each class. The positive and negative samples in each triplet are replaced by their corresponding class centers, which prompts compact class representations and benefits training stability. The idea of class-center involved loss can be extended to the pair-wise ranking loss and the quadruplet loss, which demonstrates the generalization of the proposed framework. Extensive experiments support that the PCCT framework works effectively for medical image classification with imbalanced training images. On four challenging class-imbalanced datasets (two skin datasets Skin7 and Skin 198, one chest X-ray dataset ChestXray-COVID, and one eye dataset Kaggle EyePACs), the proposed approach respectively obtains the mean F1 score 86. 20, 65. 20, 91. 32, and 87. 18 over all classes and 81. 40, 63. 87, 82. 62, and 79. 09 for rare classes, achieving state-of-the-art performance and outperforming the widely used methods for the class imbalance issue.

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