arXiv subjects

Wencheng Han

Publications and source records attributed to Wencheng Han.

31 records · Page 2Linked to original sources

arXiv2024

DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation

Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. However, many existing simulations need to predict waypoints along fixed routes on public datasets or synthetic photorealistic data, \ie, open-loop simulation usually lacks the ability to assess dynamic decision-making. While the recent efforts of closed-loop simulation offer feedback-driven environments, they cannot process visual sensor inputs or produce outputs that differ from real-world data. To address these challenges, we propose DrivingSphere, a realistic and closed-loop simulation framework. Its core idea is to build 4D world representation and generate real-life and controllable driving scenarios. In specific, our framework includes a Dynamic Environment Composition module that constructs a detailed 4D driving world with a format of occupancy equipping with static backgrounds and dynamic objects, and a Visual Scene Synthesis module that transforms this data into high-fidelity, multi-view video outputs, ensuring spatial and temporal consistency. By providing a dynamic and realistic simulation environment, DrivingSphere enables comprehensive testing and validation of autonomous driving algorithms, ultimately advancing the development of more reliable autonomous cars. The benchmark will be publicly released.

cs.RO↗

arXiv2024

RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model

Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW information. In image-based de-rendering, metadata is commonly obtained through sampling, whereas in video tasks, it is typically derived from the initial frame. The distinct metadata requirements necessitate specialized network architectures, leading to architectural incompatibilities that increase deployment complexity. In this paper, we propose RAWMamba, a Mamba-based unified framework developed for sRGB-to-RAW de-rendering across both image and video domains. The core of RAWMamba is the Unified Metadata Embedding (UME) module, which harmonizes diverse metadata types into a unified representation. In detail, a multi-perspective affinity modeling method is proposed to promote the extraction of reference information. In addition, we introduce the Local Tone-Aware Mamba (LTA-Mamba) module, which captures long-range dependencies to enable effective global propagation of metadata. Experimental results demonstrate that the proposed RAWMamba achieves state-of-the-art performance, yielding high-quality RAW data reconstruction.

cs.CV↗

arXiv2024

Towards High-Fidelity 3D Portrait Generation with Rich Details by Cross-View Prior-Aware Diffusion

Recent diffusion-based Single-image 3D portrait generation methods typically employ 2D diffusion models to provide multi-view knowledge, which is then distilled into 3D representations. However, these methods usually struggle to produce high-fidelity 3D models, frequently yielding excessively blurred textures. We attribute this issue to the insufficient consideration of cross-view consistency during the diffusion process, resulting in significant disparities between different views and ultimately leading to blurred 3D representations. In this paper, we address this issue by comprehensively exploiting multi-view priors in both the conditioning and diffusion procedures to produce consistent, detail-rich portraits. From the conditioning standpoint, we propose a Hybrid Priors Diffsion model, which explicitly and implicitly incorporates multi-view priors as conditions to enhance the status consistency of the generated multi-view portraits. From the diffusion perspective, considering the significant impact of the diffusion noise distribution on detailed texture generation, we propose a Multi-View Noise Resamplig Strategy integrated within the optimization process leveraging cross-view priors to enhance representation consistency. Extensive experiments demonstrate that our method can produce 3D portraits with accurate geometry and rich details from a single image. The project page is at \url{https://haoran-wei.github.io/Portrait-Diffusion}.

cs.CV↗

arXiv2024

High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Prior

In the area of self-supervised monocular depth estimation, models that utilize rich-resource inputs, such as high-resolution and multi-frame inputs, typically achieve better performance than models that use ordinary single image input. However, these rich-resource inputs may not always be available, limiting the applicability of these methods in general scenarios. In this paper, we propose Rich-resource Prior Depth estimator (RPrDepth), which only requires single input image during the inference phase but can still produce highly accurate depth estimations comparable to rich resource based methods. Specifically, we treat rich-resource data as prior information and extract features from it as reference features in an offline manner. When estimating the depth for a single-image image, we search for similar pixels from the rich-resource features and use them as prior information to estimate the depth. Experimental results demonstrate that our model outperform other single-image model and can achieve comparable or even better performance than models with rich-resource inputs, only using low-resolution single-image input.

cs.CV↗

arXiv2024

RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception

Concurrent processing of multiple autonomous driving 3D perception tasks within the same spatiotemporal scene poses a significant challenge, in particular due to the computational inefficiencies and feature competition between tasks when using traditional multi-task learning approaches. This paper addresses these issues by proposing a novel unified representation, RepVF, which harmonizes the representation of various perception tasks such as 3D object detection and 3D lane detection within a single framework. RepVF characterizes the structure of different targets in the scene through a vector field, enabling a single-head, multi-task learning model that significantly reduces computational redundancy and feature competition. Building upon RepVF, we introduce RFTR, a network designed to exploit the inherent connections between different tasks by utilizing a hierarchical structure of queries that implicitly model the relationships both between and within tasks. This approach eliminates the need for task-specific heads and parameters, fundamentally reducing the conflicts inherent in traditional multi-task learning paradigms. We validate our approach by combining labels from the OpenLane dataset with the Waymo Open dataset. Our work presents a significant advancement in the efficiency and effectiveness of multi-task perception in autonomous driving, offering a new perspective on handling multiple 3D perception tasks synchronously and in parallel. The code will be available at: https://github.com/jbji/RepVF

cs.CV↗

arXiv2024

AdaOcc: Adaptive Forward View Transformation and Flow Modeling for 3D Occupancy and Flow Prediction

In this technical report, we present our solution for the Vision-Centric 3D Occupancy and Flow Prediction track in the nuScenes Open-Occ Dataset Challenge at CVPR 2024. Our innovative approach involves a dual-stage framework that enhances 3D occupancy and flow predictions by incorporating adaptive forward view transformation and flow modeling. Initially, we independently train the occupancy model, followed by flow prediction using sequential frame integration. Our method combines regression with classification to address scale variations in different scenes, and leverages predicted flow to warp current voxel features to future frames, guided by future frame ground truth. Experimental results on the nuScenes dataset demonstrate significant improvements in accuracy and robustness, showcasing the effectiveness of our approach in real-world scenarios. Our single model based on Swin-Base ranks second on the public leaderboard, validating the potential of our method in advancing autonomous car perception systems.

cs.CV↗

arXiv2024

DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving

In the field of autonomous driving, two important features of autonomous driving car systems are the explainability of decision logic and the accuracy of environmental perception. This paper introduces DME-Driver, a new autonomous driving system that enhances the performance and reliability of autonomous driving system. DME-Driver utilizes a powerful vision language model as the decision-maker and a planning-oriented perception model as the control signal generator. To ensure explainable and reliable driving decisions, the logical decision-maker is constructed based on a large vision language model. This model follows the logic employed by experienced human drivers and makes decisions in a similar manner. On the other hand, the generation of accurate control signals relies on precise and detailed environmental perception, which is where 3D scene perception models excel. Therefore, a planning oriented perception model is employed as the signal generator. It translates the logical decisions made by the decision-maker into accurate control signals for the self-driving cars. To effectively train the proposed model, a new dataset for autonomous driving was created. This dataset encompasses a diverse range of human driver behaviors and their underlying motivations. By leveraging this dataset, our model achieves high-precision planning accuracy through a logical thinking process.

cs.RO↗

arXiv2023

Multi-Resolution Fusion for Fully Automatic Cephalometric Landmark Detection

Cephalometric landmark detection on lateral skull X-ray images plays a crucial role in the diagnosis of certain dental diseases. Accurate and effective identification of these landmarks presents a significant challenge. Based on extensive data observations and quantitative analyses, we discovered that visual features from different receptive fields affect the detection accuracy of various landmarks differently. As a result, we employed an image pyramid structure, integrating multiple resolutions as input to train a series of models with different receptive fields, aiming to achieve the optimal feature combination for each landmark. Moreover, we applied several data augmentation techniques during training to enhance the model's robustness across various devices and measurement alternatives. We implemented this method in the Cephalometric Landmark Detection in Lateral X-ray Images 2023 Challenge and achieved a Mean Radial Error (MRE) of 1.62 mm and a Success Detection Rate (SDR) 2.0mm of 74.18% in the final testing phase.

eess.IV↗

arXiv2023

Decoupling the Curve Modeling and Pavement Regression for Lane Detection

The curve-based lane representation is a popular approach in many lane detection methods, as it allows for the representation of lanes as a whole object and maximizes the use of holistic information about the lanes. However, the curves produced by these methods may not fit well with irregular lines, which can lead to gaps in performance compared to indirect representations such as segmentation-based or point-based methods. We have observed that these lanes are not intended to be irregular, but they appear zigzagged in the perspective view due to being drawn on uneven pavement. In this paper, we propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression. Specifically, we use a parameterized curve to represent lanes in the BEV space to reflect the original distribution of lanes. For the second part, since ground heights are determined by natural factors such as road conditions and are less holistic, we regress the ground heights of key points separately from the curve modeling. Additionally, we have unified the 2D and 3D lane detection tasks by designing a new framework and a series of losses to guide the optimization of models with or without 3D lane labels. Our experiments on 2D lane detection benchmarks (TuSimple and CULane), as well as the recently proposed 3D lane detection datasets (ONCE-3Dlane and OpenLane), have shown significant improvements. We will make our well-documented source code publicly available.

cs.CV↗

arXiv2023

Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network

Monocular depth estimation is known as an ill-posed task in which objects in a 2D image usually do not contain sufficient information to predict their depth. Thus, it acts differently from other tasks (e.g., classification and segmentation) in many ways. In this paper, we find that self-supervised monocular depth estimation shows a direction sensitivity and environmental dependency in the feature representation. But the current backbones borrowed from other tasks pay less attention to handling different types of environmental information, limiting the overall depth accuracy. To bridge this gap, we propose a new Direction-aware Cumulative Convolution Network (DaCCN), which improves the depth feature representation in two aspects. First, we propose a direction-aware module, which can learn to adjust the feature extraction in each direction, facilitating the encoding of different types of information. Secondly, we design a new cumulative convolution to improve the efficiency for aggregating important environmental information. Experiments show that our method achieves significant improvements on three widely used benchmarks, KITTI, Cityscapes, and Make3D, setting a new state-of-the-art performance on the popular benchmarks with all three types of self-supervision.

cs.CV↗

arXiv2023

Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency

Monocular 3D object detection has become a mainstream approach in automatic driving for its easy application. A prominent advantage is that it does not need LiDAR point clouds during the inference. However, most current methods still rely on 3D point cloud data for labeling the ground truths used in the training phase. This inconsistency between the training and inference makes it hard to utilize the large-scale feedback data and increases the data collection expenses. To bridge this gap, we propose a new weakly supervised monocular 3D objection detection method, which can train the model with only 2D labels marked on images. To be specific, we explore three types of consistency in this task, i.e. the projection, multi-view and direction consistency, and design a weakly-supervised architecture based on these consistencies. Moreover, we propose a new 2D direction labeling method in this task to guide the model for accurate rotation direction prediction. Experiments show that our weakly-supervised method achieves comparable performance with some fully supervised methods. When used as a pre-training method, our model can significantly outperform the corresponding fully-supervised baseline with only 1/3 3D labels. https://github.com/weakmono3d/weakmono3d

cs.CV↗

arXiv2023

Referring Multi-Object Tracking

Existing referring understanding tasks tend to involve the detection of a single text-referred object. In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT). Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking. To the best of our knowledge, it is the first work to achieve an arbitrary number of referent object predictions in videos. To push forward RMOT, we construct one benchmark with scalable expressions based on KITTI, named Refer-KITTI. Specifically, it provides 18 videos with 818 expressions, and each expression in a video is annotated with an average of 10.7 objects. Further, we develop a transformer-based architecture TransRMOT to tackle the new task in an online manner, which achieves impressive detection performance and outperforms other counterparts. The dataset and code will be available at https://github.com/wudongming97/RMOT.

cs.CV↗

arXiv2021

Learning to Fuse Asymmetric Feature Maps in Siamese Trackers

Recently, Siamese-based trackers have achieved promising performance in visual tracking. Most recent Siamese-based trackers typically employ a depth-wise cross-correlation (DW-XCorr) to obtain multi-channel correlation information from the two feature maps (target and search region). However, DW-XCorr has several limitations within Siamese-based tracking: it can easily be fooled by distractors, has fewer activated channels, and provides weak discrimination of object boundaries. Further, DW-XCorr is a handcrafted parameter-free module and cannot fully benefit from offline learning on large-scale data. We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on large-scale data. Different from DW-XCorr and its predecessor(XCorr), which regard a single feature map as the convolution kernel, our ACM decomposes the convolution operation on a concatenated feature map into two mathematically equivalent operations, thereby avoiding the need for the feature maps to be of the same size (width and height)during concatenation. Our ACM can incorporate useful prior information, such as bounding-box size, with standard visual features. Furthermore, ACM can easily be integrated into existing Siamese trackers based on DW-XCorror XCorr. To demonstrate its generalization ability, we integrate ACM into three representative trackers: SiamFC, SiamRPN++, and SiamBAN. Our experiments reveal the benefits of the proposed ACM, which outperforms existing methods on six tracking benchmarks. On the LaSOT test set, our ACM-based tracker obtains a significant improvement of 5.8% in terms of success (AUC), over the baseline.

cs.CV↗