5 citations · 10 across the 12 of their papers we have counts for
14 papers
DynStream: Online Streaming 4D Gaussian Reconstruction of Dynamic Worlds from Unposed Video
Dingwei Xian, Xiaoyu Zhou, Yajiao Xiong +2
Online reconstruction of dynamic 4D scenes from long, unposed streaming videos requires both continuous processing and photorealistic rendering, which existing methods struggle to…
Zero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting
Zinan Lv, Yeqian Qian, Chen Sang +3
UAV navigation in unstructured outdoor environments using passive monocular vision is hindered by the substantial visual domain gap between simulation and reality. While 3D Gaussia…
KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System
Zhongyu Xia, Wenhao Chen, Yongtao Wang +1
Visual-language reasoning, driving knowledge, and value alignment are essential for advanced autonomous driving systems. However, existing approaches largely rely on data-driven le…
HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving
Zhongyu Xia, Zhiwei Lin, Yongtao Wang +1
Three-dimensional feature extraction and multi-task perception are fundamental components of modern autonomous driving systems. Although large image encoders, high-resolution input…
EA3D: Online Open-World 3D Object Extraction from Streaming Videos
Xiaoyu Zhou, Jingqi Wang, Yuang Jia +3
Current 3D scene understanding methods are limited by offline-collected multi-view data or pre-constructed 3D geometry. In this paper, we present ExtractAnything3D (EA3D), a unifie…
DrivingGaussian++: Towards Realistic Reconstruction and Editable Simulation for Surrounding Dynamic Driving Scenes
Yajiao Xiong, Xiaoyu Zhou, Yongtao Wan +2
We present DrivingGaussian++, an efficient and effective framework for realistic reconstructing and controllable editing of surrounding dynamic autonomous driving scenes. DrivingGa…
AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting
Xiaoyu Zhou, Jingqi Wang, Yongtao Wang +3
Obtaining high-quality 3D semantic occupancy from raw sensor data remains an essential yet challenging task, often requiring extensive manual labeling. In this work, we propose Aut…
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
Zhongyu Xia, Jishuo Li, Zhiwei Lin +3
Open-world perception aims to develop a model adaptable to novel domains and various sensor configurations and can understand uncommon objects and corner cases. However, current re…
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view Cameras
Zhongyu Xia, ZhiWei Lin, Xinhao Wang +5
Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird's-eye-vie…
GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting
Xiaoyu Zhou, Xingjian Ran, Yajiao Xiong +5
We present GALA3D, generative 3D GAussians with LAyout-guided control, for effective compositional text-to-3D generation. We first utilize large language models (LLMs) to generate…
Fine-grained Controllable Video Generation via Object Appearance and Context
Hsin-Ping Huang, Yu-Chuan Su, Deqing Sun +4
Text-to-video generation has shown promising results. However, by taking only natural languages as input, users often face difficulties in providing detailed information to precise…
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes
Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan +3
We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially an…
Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
Junyi Zhang, Charles Herrmann, Junhwa Hur +4
While pre-trained large-scale vision models have shown significant promise for semantic correspondence, their features often struggle to grasp the geometry and orientation of insta…
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
Zhiwei Lin, Yongtao Wang, Shengxiang Qi +2
Existing LiDAR-based 3D object detection methods for autonomous driving scenarios mainly adopt the training-from-scratch paradigm. Unfortunately, this paradigm heavily relies on la…