15 citations · 65 across the 21 of their papers we have counts for
22 papers
Native and Compact Structured Latents for 3D Generation
Jianfeng Xiang, Xiaoxue Chen, Sicheng Xu +8
Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to cap…
DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images
Xiaoxue Chen, Ziyi Xiong, Yuantao Chen +11
Autonomous driving needs fast, scalable 4D reconstruction and re-simulation for training and evaluation, yet most methods for dynamic driving scenes still rely on per-scene optimiz…
CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting
Haoran Xu, Saining Zhang, Peishuo Li +15
Vehicle-to-everything (V2X) communication plays a crucial role in autonomous driving, enabling cooperation between vehicles and infrastructure. While simulation has significantly c…
InvRGB+L: Inverse Rendering of Complex Scenes with Unified Color and LiDAR Reflectance Modeling
Xiaoxue Chen, Bhargav Chandaka, Chih-Hao Lin +4
We present InvRGB+L, a novel inverse rendering model that reconstructs large, relightable, and dynamic scenes from a single RGB+LiDAR sequence. Conventional inverse graphics method…
Controllable Radar Simulation with Waveform Parameter Embedding
Weiqing Xiao, Hao Huang, Chonghao Zhong +9
Autonomous driving simulators still lack high-fidelity radar, even though radar is critical for robust perception in adverse weather. A key obstacle is that raw radar point clouds…
Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors
Siqi Li, Xiaoxue Chen, Haoyu Cheng +3
Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challen…
RGM: Reconstructing High-fidelity 3D Car Assets with Relightable 3D-GS Generative Model from a Single Image
Xiaoxue Chen, Jv Zheng, Hao Huang +8
The generation of high-quality 3D car assets is essential for various applications, including video games, autonomous driving, and virtual reality. Current 3D generation methods ut…
Drone-assisted Road Gaussian Splatting with Cross-view Uncertainty
Saining Zhang, Baijun Ye, Xiaoxue Chen +5
Robust and realistic rendering for large-scale road scenes is essential in autonomous driving simulation. Recently, 3D Gaussian Splatting (3D-GS) has made groundbreaking progress i…
Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids
Junchen Liu, Wenbo Hu, Zhuo Yang +6
Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge t…
Spectrally Pruned Gaussian Fields with Neural Compensation
Runyi Yang, Zhenxin Zhu, Zhou Jiang +6
Recently, 3D Gaussian Splatting, as a novel 3D representation, has garnered attention for its fast rendering speed and high rendering quality. However, this comes with high memory…
Ultraman: Single Image 3D Human Reconstruction with Ultra Speed and Detail
Mingjin Chen, Junhao Chen, Xiaojun Ye +4
3D human body reconstruction has been a challenge in the field of computer vision. Previous methods are often time-consuming and difficult to capture the detailed appearance of the…
NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields
Xiaoxue Chen, Junchen Liu, Hao Zhao +2
Neural radiance fields (NeRF) have revolutionized the field of image-based view synthesis. However, NeRF uses straight rays and fails to deal with complicated light path changes ca…
ECT: Fine-grained Edge Detection with Learned Cause Tokens
Shaocong Xu, Xiaoxue Chen, Yuhang Zheng +4
In this study, we tackle the challenging fine-grained edge detection task, which refers to predicting specific edges caused by reflectance, illumination, normal, and depth changes,…
STRAP: Structured Object Affordance Segmentation with Point Supervision
Leiyao Cui, Xiaoxue Chen, Hao Zhao +2
With significant annotation savings, point supervision has been proven effective for numerous 2D and 3D scene understanding problems. This success is primarily attributed to the st…
DPF: Learning Dense Prediction Fields with Weak Supervision
Xiaoxue Chen, Yuhang Zheng, Yupeng Zheng +4
Nowadays, many visual scene understanding problems are addressed by dense prediction networks. But pixel-wise dense annotations are very expensive (e.g., for scene parsing) or impo…
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds
Huan-ang Gao, Beiwen Tian, Pengfei Li +5
Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still…
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
Pengfei Li, Beiwen Tian, Yongliang Shi +4
Current referring expression comprehension algorithms can effectively detect or segment objects indicated by nouns, but how to understand verb reference is still under-explored. As…
Understanding Embodied Reference with Touch-Line Transformer
Yang Li, Xiaoxue Chen, Hao Zhao +4
We study embodied reference understanding, the task of locating referents using embodied gestural signals and language references. Human studies have revealed that objects referred…
Distance-Aware Occlusion Detection with Focused Attention
Yang Li, Yucheng Tu, Xiaoxue Chen +2
For humans, understanding the relationships between objects using visual signals is intuitive. For artificial intelligence, however, this task remains challenging. Researchers have…
SNAKE: Shape-aware Neural 3D Keypoint Field
Chengliang Zhong, Peixing You, Xiaoxue Chen +6
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detecti…
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing
Xiaoxue Chen, Tianyu Liu, Hao Zhao +2
Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance. In this paper, we tack…
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
Xiaoxue Chen, Hao Zhao, Guyue Zhou +1
3D scene understanding from point clouds plays a vital role for various robotic applications. Unfortunately, current state-of-the-art methods use separate neural networks for diffe…