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Chenyang Lei

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8 papers

2 author rows

AAAI Conference 2025 Conference Paper

FIRM: Flexible Interactive Reflection ReMoval

Xiao Chen
Xudong Jiang
Yunkang Tao
Zhen Lei
Qing Li
Chenyang Lei
Zhaoxiang Zhang

Removing reflection from a single image is challenging due to the absence of general reflection priors. Although existing methods incorporate extensive user guidance for satisfactory performance, they often lack the flexibility to adapt user guidance in different modalities, and dense user interactions further limit their practicality. To alleviate these problems, this paper presents FIRM, a novel framework for Flexible Interactive image Reflection reMoval with various forms of guidance, where users can provide sparse visual guidance (e.g., points, boxes, or strokes) or text descriptions for better reflection removal. Firstly, we design a novel user guidance conversion module (UGC) to transform different forms of guidance into unified contrastive masks. The contrastive masks provide explicit cues for identifying reflection and transmission layers in blended images. Secondly, we devise a contrastive mask-guided reflection removal network that comprises a newly proposed contrastive guidance interaction block (CGIB). This block leverages a unique cross-attention mechanism that merges contrastive masks with image features, allowing for precise layer separation. The proposed framework requires only 10% of the guidance time needed by previous interactive methods, which makes a step-change in flexibility. Extensive results on public real-world reflection removal datasets validate that our method demonstrates state-of-the-art reflection removal performance.

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AAAI Conference 2024 Conference Paper

A Diffusion Model with State Estimation for Degradation-Blind Inverse Imaging

Liya Ji
Zhefan Rao
Sinno Jialin Pan
Chenyang Lei
Qifeng Chen

Solving the task of inverse imaging problems can restore unknown clean images from input measurements that have incomplete information. Utilizing powerful generative models, such as denoising diffusion models, could better tackle the ill-posed issues of inverse problems with the distribution prior of the unknown clean images. We propose a learnable state-estimator-based diffusion model to incorporate the measurements into the reconstruction process. Our method makes efficient use of the pre-trained diffusion models with computational feasibility compared to the conditional diffusion models, which need to be trained from scratch. In addition, our pipeline does not require explicit knowledge of the image degradation operator or make the assumption of its form, unlike many other works that use the pre-trained diffusion models at the test time. The experiments on three typical inverse imaging problems (both linear and non-linear), inpainting, deblurring, and JPEG compression restoration, have comparable results with the state-of-the-art methods.

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NeurIPS Conference 2024 Conference Paper

Adaptive Domain Learning for Cross-domain Image Denoising

Zian Qian
Chenyang Qi
Ka L. Law
Hao Fu
Chenyang Lei
Qifeng Chen

Different camera sensors have different noise patterns, and thus an image denoising model trained on one sensor often does not generalize well to a different sensor. One plausible solution is to collect a large dataset for each sensor for training or fine-tuning, which is inevitably time-consuming. To address this cross-domain challenge, we present a novel adaptive domain learning (ADL) scheme for cross-domain RAW image denoising by utilizing existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain). The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain (some data are harmful as adding them during training lowers the performance due to domain gaps). Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising. We conduct extensive experiments on public datasets with various smartphone and DSLR cameras, which show our proposed model outperforms prior work on cross-domain image denoising, given a small amount of image data from the target domain sensor.

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IROS Conference 2024 Conference Paper

CTS: Sim-to-Real Unsupervised Domain Adaptation on 3D Detection

Meiying Zhang
Weiyuan Peng
Guangyao Ding
Chenyang Lei
Chunlin Ji
Qi Hao

Simulation data can be accurately labeled and have been expected to improve the performance of data-driven algorithms, including object detection. However, due to the various domain inconsistencies from simulation to reality (sim-to-real), cross-domain object detection algorithms usually suffer from dramatic performance drops. While numerous unsupervised domain adaptation (UDA) methods have been developed to address cross-domain tasks between real-world datasets, progress in sim-to-real remains limited. This paper presents a novel Complex-to-Simple (CTS) framework to transfer models from labeled simulation (source) to unlabeled reality (target) domains. Based on a two-stage detector, the novelty of this work is threefold: 1) developing fixed-size anchor heads and RoI augmentation to address size bias and feature diversity between two domains, thereby improving the quality of pseudo-label; 2) developing a novel corner-format representation of aleatoric uncertainty (AU) for the bounding box, to uniformly quantify pseudo-label quality; 3) developing a noise-aware mean teacher domain adaptation method based on AU, as well as object-level and frame-level sampling strategies, to migrate the impact of noisy labels. Experimental results demonstrate that our proposed approach significantly enhances the sim-to-real domain adaptation capability of 3D object detection models, outperforming state-of-the-art cross-domain algorithms, which are usually developed for real-to-real UDA tasks.

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NeurIPS Conference 2024 Conference Paper

DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos

Linhan Wang
Kai Cheng
Shuo Lei
Shengkun Wang
Wei Yin
Chenyang Lei
Xiaoxiao Long
Chang-Tien Lu

We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenarios, existing methods are primarily designed for videos collected by autonomous vehicles. However, these videos are limited in both quantity and diversity compared to dash cam videos, which are more widely used across various types of vehicles and capture a broader range of scenarios. Dash cam videos often suffer from severe obstructions such as reflections and occlusions on the windshields, which significantly impede the application of neural rendering techniques. To address this challenge, we develop DC-Gaussian based on the recent real-time neural rendering technique 3D Gaussian Splatting (3DGS). Our approach includes an adaptive image decomposition module to model reflections and occlusions in a unified manner. Additionally, we introduce illumination-aware obstruction modeling to manage reflections and occlusions under varying lighting conditions. Lastly, we employ a geometry-guided Gaussian enhancement strategy to improve rendering details by incorporating additional geometry priors. Experiments on self-captured and public dash cam videos show that our method not only achieves state-of-the-art performance in novel view synthesis, but also accurately reconstructing captured scenes getting rid of obstructions.

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ICRA Conference 2023 Conference Paper

Scene-level Point Cloud Colorization with Semantics-and-geometry-aware Networks

Rongrong Gao
Tian-Zhu Xiang
Chenyang Lei
Jaesik Park
Qifeng Chen 0001

In robotic applications, we often obtain tons of 3D point cloud data without color information, and it is difficult to visualize point clouds in a meaningful and colorful way. Can we colorize 3D point clouds for better visualization? Existing deep learning-based colorization methods usually only take simple 3D objects as input, and their performance for complex scenes with multiple objects is limited. To this end, this paper proposes a novel semantics-and-geometry-aware colorization network, termed SGNet, for vivid scene-level point cloud colorization. Specifically, we propose a novel pipeline that explores geometric and semantic cues from point clouds containing only coordinates for color prediction. We also design two novel losses, including a colorfulness metric loss and a pairwise consistency loss, to constrain model training for genuine colorization. To the best of our knowledge, our work is the first to generate realistic colors for point clouds of large-scale indoor scenes. Extensive experiments on the widely used ScanNet benchmarks demonstrate that the proposed method achieves state-of-the-art performance on point cloud colorization.

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NeurIPS Conference 2020 Conference Paper

Blind Video Temporal Consistency via Deep Video Prior

Chenyang Lei
Yazhou Xing
Qifeng Chen

Applying image processing algorithms independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. Unlike most previous methods that enforce temporal consistency with optical flow, we show that temporal consistency can be achieved by training a convolutional network on a video with the Deep Video Prior. Moreover, a carefully designed iteratively reweighted training strategy is proposed to address the challenging multimodal inconsistency problem. We demonstrate the effectiveness of our approach on 7 computer vision tasks on videos. Extensive quantitative and perceptual experiments show that our approach obtains superior performance than state-of-the-art methods on blind video temporal consistency.

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IROS Conference 2020 Conference Paper

Video Depth Estimation by Fusing Flow-to-Depth Proposals

Jiaxin Xie
Chenyang Lei
Zhuwen Li
Li Erran Li
Qifeng Chen 0001

Depth from a monocular video can enable billions of devices and robots with a single camera to see the world in 3D. In this paper, we present a model for video depth estimation, which consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network. Given optical flow and camera poses, our flow-to-depth layer generates depth proposals and their corresponding confidence maps by explicitly solving an epipolar geometry optimization problem. Our flow-to-depth layer is differentiable, and thus we can refine camera poses by maximizing the aggregated confidence in the camera pose refinement module. Our depth fusion network can utilize the target frame, depth proposals, and confidence maps inferred from different neighboring frames to produce the final depth map. Furthermore, the depth fusion network can additionally take the depth proposals generated by other methods to further improve the results. The experiments on three public datasets show that our approach outperforms state-of-the-art depth estimation methods, and has reasonable crossdataset generalization ability: our model trained on KITTI still performs well on the unseen Waymo dataset.

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