Author name cluster

Yulan Guo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Edge-Centric Relational Reasoning for 3D Scene Graph Prediction

Yanni Ma
Hao Liu
Yulan Guo
Theo Gevers
Martin R. Oswald

3D scene graph prediction aims to abstract complex 3D environments into structured graphs consisting of objects and their pairwise relationships. Existing approaches typically adopt object-centric graph neural networks, where relation edge features are iteratively updated by aggregating messages from connected object nodes. However, this design inherently restricts relation representations to pairwise object context, making it difficult to capture high-order relational dependencies that are essential for accurate relation prediction. To address this limitation, we propose a Link-guided Edge-centric relational reasoning framework with Object-aware fusion, namely LEO, which enables progressive reasoning from relation-level context to object-level understanding. Specifically, LEO first predicts potential links between object pairs to suppress irrelevant edges, and then transforms the original scene graph into a line graph where each relation is treated as a node. A line graph neural network is applied to perform edge-centric relational reasoning to capture inter-relation context. The enriched relation features are subsequently integrated into the original object-centric graph to enhance object-level reasoning and improve relation prediction. Our framework is model-agnostic and can be integrated with any existing object-centric method. Experiments on the 3DSSG dataset with two competitive baselines show consistent improvements, highlighting the effectiveness of our edge-to-object reasoning paradigm.

PDF Details DOI

ICRA Conference 2025 Conference Paper

$U^2$ Frame: A Unified and Unsupervised Learning Framework for LiDAR-Based Loop Closing

Yixin Zhang
Sheng Ao
Ye Zhang 0037
Zhuo Song
Qingyong Hu
Tao Chang
Yulan Guo

Loop closing is critically important in Simultaneous Localization and Mapping (SLAM) due to its ability to correct accumulated localization errors. However, existing methods are hindered by the difficulty of acquiring pose labels and the unreliability of ground truth data. In this paper, we propose $U^{2}$ Frame, a unified LiDAR-based loop closing framework that handles both loop closure detection and relative pose estimation without any ground truth training data. Specifically, the natural temporal-spatial correlation in point cloud sequences is first leveraged to supervise the network training, where near scans are treated as positives and vice versa as negatives. A new neural architecture is then constructed to jointly learn highly discriminative local and global features for loop closure detection. Additionally, an effective candidate verification module that exploits high-order geometric information is presented to further filter out false loop closures and estimate precise poses. We extensively evaluate $U^{2}$ Frame on multiple datasets according to two tasks derived from loop closing: loop closure detection and loop pose estimation. Comparative experiments demonstrate that our method outperforms existing state-of-the-art supervised techniques and has a strong generalization ability across unseen scenarios. Our code is released at https://github.com/yxin-zhang/U2Frame.

Details

ICRA Conference 2025 Conference Paper

3D Whole-Body Pose Estimation Using Graph High-Resolution Network for Humanoid Robot Teleoperation

Mingyu Zhang
Qing Gao 0002
Yuanchuan Lai
Ye Zhang 0037
Tao Chang
Yulan Guo

In the realm of robotics, teleoperation plays a pivotal role in performing high-risk or intricate tasks, and obtaining precise 3D whole-body pose is crucial for this purpose. Traditional two-stage methods have limitations in estimating different body parts, leading to complex systems and higher estimation errors. In order to address these issues, the paper introduces a novel framework called Graph High-Resolution Network (GraphHRNet) for accurate 3D whole-body pose estimation, which is essential for the teleoperation of humanoid robots. GraphHRNet effectively captures global structural information and local details by integrating a High-Resolution Module and a Multi-branch Regression Module. The High-Resolution Module utilizes an enhanced graph convolution kernel to fuse multi-scale features, capturing global information, while the Multi-branch Regression Module focuses on refining and predicting accurate 3D coordinates for intricate body parts such as hands and face. Experimental results on the H3WB dataset demonstrate that GraphHRNet surpasses state-of-the-art (SOTA) methods in 3D whole-body pose estimation, significantly improving performance. Furthermore, the paper explores the potential application of this approach in a tele-operation system for humanoid robots, providing an intuitive and high-fidelity solution for remotely executing complex tasks. The code have been publicly available at https://github.com/Z-mingyu/GraphHRNet.git

Details

AAAI Conference 2025 Conference Paper

AIQViT: Architecture-Informed Post-Training Quantization for Vision Transformers

Runqing Jiang
Ye Zhang
Longguang Wang
Pengpeng Yu
Yulan Guo

Post-training quantization (PTQ) has emerged as a promising solution for reducing the storage and computational cost of vision transformers (ViTs). Recent advances primarily target at crafting quantizers to deal with peculiar activations characterized by ViTs. However, most existing methods underestimate the information loss incurred by weight quantization, resulting in significant performance deterioration, particularly in low-bit cases. Furthermore, a common practice in quantizing post-Softmax activations of ViTs is to employ logarithmic transformations, which unfortunately prioritize less informative values around zero. This approach introduces additional redundancies, ultimately leading to suboptimal quantization efficacy. To handle these, this paper proposes an innovative PTQ method tailored for ViTs, termed AIQViT (Architecture-Informed Post-training Quantization for ViTs). First, we design an architecture-informed low-rank compensation mechanism, wherein learnable low-rank weights are introduced to compensate for the degradation caused by weight quantization. Second, we design a dynamic focusing quantizer to accommodate the unbalanced distribution of post-Softmax activations, which dynamically selects the most valuable interval for higher quantization resolution. Extensive experiments on five vision tasks, including image classification, object detection, instance segmentation, point cloud classification, and point cloud part segmentation, demonstrate the superiority of AIQViT over state-of-the-art PTQ methods.

PDF Details DOI

IROS Conference 2025 Conference Paper

Graph2Scene: Versatile 3D Indoor Scene Generation with Interaction-aware Scene Graph

Minglin Chen
Rongkun Yang
Qibin Hu
Kaiwen Xue
Shunbo Zhou
Yulan Guo

Embodied artificial intelligence requires a wide variety of large-scale simulated environments for development. Previous scene reconstruction approaches based on multiview images can produce high-fidelity 3D scenes but lack diversity. In contrast, existing prompt-based scene generation approaches can produce diverse scenes but lack fine-grained controls. To bridge these two fields, the scene graph provides the key relationships within a scene, while offering flexible controls. However, 3D scene generation from scene graphs is challenging and under-explored. In this paper, we propose a scene graph-based 3D indoor scene generation method for the efficient simulated environment creation, that maintains both high diversity and fine-grained control. Specifically, we first introduce an interaction-aware scene graph to merge object nodes with hierarchical interaction relationships, which alleviates levitation and interference issues during the scene generation. Then, we employ the large language model (LLM) for instruction-driven 3D layout generation with carefully designed prompts. Finally, a 3D large generation model is utilized to generate the content for each node of the interaction-aware scene graph, which is then transformed based on the corresponding bounding box in the 3D layout. The experiments demonstrate that the proposed method achieves state-of-the-art performance on 3D indoor scene generation. Additionally, the proposed method exhibits fine-grained controls at the object level, while providing a high diversity of layouts, geometry, and textures.

Details

ICRA Conference 2025 Conference Paper

Multi-Modality Test-Time Adaptation for Semantic Segmentation in Robotic Perception

Yan Liu 0043
Hongyuan Zhu 0002
Ye Zhang 0037
Yinjie Lei
Yulan Guo

Test-Time Adaptation (TTA) adjusts pre-trained models in unlabeled unseen environments during the test phase, making it more practical for robotic applications. However, the constant changes of the physical world create significant domain gaps between the received data during robot deployment and the source data used for training. In addition, existing methods mainly focus on a single modality, e. g. , RGB images, limiting the application of these methods in multi-modality input scenarios. In this work, we propose a Deep Multi-modality Aggregation Test-time Adaptation (DMATA) method to address the above mentioned issues. To prevent the domain shifts from disrupting the adaptation process, we first propose a Momentum-based Teacher-Student (MTS) framework. Since the teacher model and the student model contain complementary information, we design an Uncertainty-Guide (UG) feature fusion block to fuse the representation of the teacher model and student model of each modality. Finally, we introduce a 3D-Guide-2D (3G2) feature fusion block to leverage spatial information for enhancing 2D feature representation. Extensive experiments across three scenarios, including sensor-to-sensor, day-to-night, and city-to-city, demonstrate the effectiveness of our method in TTA multi-modality semantic segmentation tasks. Notably, under the scenario of sensor-to-sensor adaptation, our proposed DMATA obtains an $m$ IoU of 54. 2%, which is superior to the state-of-the-art test-time adaptation method.

Details

IROS Conference 2025 Conference Paper

Self-Distilled Stereo Matching: Real-Time Domain Generalization for Robotic Depth Perception

Xuxin Zhang
Kunhong Li 0001
Yongjian Zhang
Zhuo Song
Runqing Jiang
Ye Zhang 0037
Yulan Guo

While human vision inherently achieves robust cross-domain depth estimation through binocular coordination, robotic systems employing stereo matching still confront significant challenges in maintaining robustness across domains when performing real-time environmental depth perception. Furthermore, most stereo matching methods struggle with challenging regions such as object boundaries and non-overlapping areas on the left side of the left image, resulting in disparity maps that are relatively indistinct and lacking fine details. In this paper, we propose Learning More in Challenging Areas (LMC) to alleviate this problem, which enhances the domain generalization of the model through targeted training on challenging regions. LMC is a simple yet effective data-driven training framework primarily based on self-distillation. Specifically, 1) We pre-train models on a high-frequency dataset to improve perception ability on object boundaries; 2) We develop a self-distillation training strategy to benefit learning in non-overlapping areas on the left side of the left image; 3) We design an adaptive difficult area mask to balance the loss weight on other undefined challenging regions. Under our proposed training framework, GwcNet achieves 33% and 23% performance improvements in autonomous driving benchmarks KITTI 2012 and KITTI 2015 respectively, while preserving real-time inference efficiency without computational overhead.

Details

ICLR Conference 2024 Conference Paper

ACRF: Compressing Explicit Neural Radiance Fields via Attribute Compression

Guangchi Fang
Qingyong Hu
Longguang Wang
Yulan Guo

In this work, we study the problem of explicit NeRF compression. Through analyzing recent explicit NeRF models, we reformulate the task of explicit NeRF compression as 3D data compression. We further introduce our NeRF compression framework, Attributed Compression of Radiance Field (ACRF), which focuses on the compression of the explicit neural 3D representation. The neural 3D structure is pruned and converted to points with features, which are further encoded using importance-guided feature encoding. Furthermore, we employ an importance-prioritized entropy model to estimate the probability distribution of transform coefficients, which are then entropy coded with an arithmetic coder using the predicted distribution. Within this framework, we present two models, ACRF and ACRF-F, to strike a balance between compression performance and encoding time budget. Our experiments, which include both synthetic and real-world datasets such as Synthetic-NeRF and Tanks&Temples, demonstrate the superior performance of our proposed algorithm.

Details

IROS Conference 2020 Conference Paper

ARPDR: An Accurate and Robust Pedestrian Dead Reckoning System for Indoor Localization on Handheld Smartphones

Xiaoqiang Teng
Pengfei Xu 0013
Deke Guo
Yulan Guo
Runbo Hu
Hua Chai
Didi Chuxing

The proliferation of mobile computing has prompted Pedestrian Dead Reckoning (PDR) to be one of the most attractive and promising indoor localization techniques for ubiquitous applications. The existing PDR approaches either suffer position drifts caused by accumulative errors or are sensitive to various users. This paper presents ARPDR, an accurate and robust PDR approach to improve the accuracy and robustness of indoor localization methods. Particularly, we propose a novel step counting algorithm based on motion models by deeply exploiting inertial sensor data. We then combine step counting with adaptive thresholding to personalize the PDR system for different users. Furthermore, we propose a novel stride-heading model with a deep neural network to predict stride lengths and walking orientations, thus the displacement errors are significantly reduced. Extensive experiments on public datasets demonstrate that ARPDR outperforms the state-of-the-art PDR methods.

Details