8 citations · 18 across the 16 of their papers we have counts for
17 papers
Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
Shiyao Qian, Yuan Ren, Dongfeng Bai +1
Simulation is essential for autonomous driving, yet current frameworks often model vehicles as rigid assets and fail to capture part-level articulation. With perception algorithms…
UniScale: Unified Scale-Aware 3D Reconstruction for Multi-View Understanding via Prior Injection for Robotic Perception
Mohammad Mahdavian, Gordon Tan, Binbin Xu +3
We present UniScale, a unified, scale-aware multi-view 3D reconstruction framework for robotic applications that flexibly integrates geometric priors through a modular, semanticall…
UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation
Zhaodong Jiang, Ashish Sinha, Tongtong Cao +3
Estimating the 6D pose of novel objects is a fundamental yet challenging problem in robotics, often relying on access to object CAD models. However, acquiring such models can be co…
HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation
Yibo Liu, Zhaodong Jiang, Binbin Xu +7
This work focuses on model-free zero-shot 6D object pose estimation for robotics applications. While existing methods can estimate the precise 6D pose of objects, they heavily rely…
UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations
Yuan Ren, Guile Wu, Runhao Li +5
Urban scene reconstruction is crucial for real-world autonomous driving simulators. Although existing methods have achieved photorealistic reconstruction, they mostly focus on pinh…
3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications
Eduardo R. Corral-Soto, Yang Liu, Tongtong Cao +2
In Autonomous Driving (AD) Perception, cyclists are considered safety-critical scene objects. Commonly used publicly-available AD datasets typically contain large amounts of car an…
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion
Shiqi Tan, Hamidreza Fazlali, Yixuan Xu +2
Range-View(RV)-based 3D point cloud segmentation is widely adopted due to its compact data form. However, RV-based methods fall short in providing robust segmentation for the occlu…
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving
Yibo Liu, Zheyuan Yang, Guile Wu +5
Generating 3D vehicle assets from in-the-wild observations is crucial to autonomous driving. Existing image-to-3D methods cannot well address this problem because they learn genera…
AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction
Mustafa Khan, Hamidreza Fazlali, Dhruv Sharma +4
Realistic scene reconstruction and view synthesis are essential for advancing autonomous driving systems by simulating safety-critical scenarios. 3D Gaussian Splatting excels in re…
Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation
Zheyuan Yang, Yibo Liu, Guile Wu +4
We present a solution for 3D object generation of ICCV 2023 OmniObject3D Challenge. In recent years, 3D object generation has made great process and achieved promising results, but…
MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving
Yibo Liu, Kelly Zhu, Guile Wu +4
Reconstructing 3D vehicles from noisy and sparse partial point clouds is of great significance to autonomous driving. Most existing 3D reconstruction methods cannot be directly app…
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation
Yixuan Xu, Hamidreza Fazlali, Yuan Ren +1
LiDAR-based 3D object detection and panoptic segmentation are two crucial tasks in the perception systems of autonomous vehicles and robots. In this paper, we propose All-in-One Pe…
PCGen: Point Cloud Generator for LiDAR Simulation
Chenqi Li, Yuan Ren, Bingbing Liu
Data is a fundamental building block for LiDAR perception systems. Unfortunately, real-world data collection and annotation is extremely costly & laborious. Recently, real data bas…
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation
Hamidreza Fazlali, Yixuan Xu, Yuan Ren +1
3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation inf…
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network
Ryan Razani, Ran Cheng, Enxu Li +3
Panoptic segmentation as an integrated task of both static environmental understanding and dynamic object identification, has recently begun to receive broad research interest. In…
S3Net: 3D LiDAR Sparse Semantic Segmentation Network
Ran Cheng, Ryan Razani, Yuan Ren +1
Semantic Segmentation is a crucial component in the perception systems of many applications, such as robotics and autonomous driving that rely on accurate environmental perception…
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds
Ran Cheng, Christopher Agia, Yuan Ren +2
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a tre…