35 citations · 39 across the 16 of their papers we have counts for
18 papers · 1 filter
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance
Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang +10
Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation and various perception tasks…
MORE: Learning Motion-Oriented Registers for Driving World Models and Planning
Yingdong Gu, Haoyu Wang, Shaocheng Yan +9
By predicting future states, latent world models provide rich self-supervision for end-to-end driving, but often inherit dense features from pretrained visual encoders as their wor…
RISE: Adaptive Imagination for World Action Models
Hongbo Lu, Liang Yao, Chenghao He +5
World Action Models (WAMs) improve planning by incorporating future world evolution into action generation, yet existing methods allocate a fixed imagination budget to every scene.…
The DAWN of World-Action Interactive Models
Hongbo Lu, Liang Yao, Chenghao He +6
A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action Models (WAMs) largely miss th…
VisionNVS: Self-Supervised Inpainting for Novel View Synthesis under the Virtual-Shift Paradigm
Hongbo Lu, Liang Yao, Chenghao He +4
A fundamental bottleneck in Novel View Synthesis (NVS) for autonomous driving is the inherent supervision gap on novel trajectories: models are tasked with synthesizing unseen view…
VLM-Loc: Localization in Point Cloud Maps via Vision-Language Models
Shuhao Kang, Youqi Liao, Peijie Wang +5
Text-to-point-cloud (T2P) localization aims to infer precise spatial positions within 3D point cloud maps from natural language descriptions, reflecting how humans perceive and com…
Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs
Huan Zheng, Yucheng Zhou, Tianyi Yan +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis. However, their application in gastrointestinal endoscopy is currently hin…
Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
Dubing Chen, Huan Zheng, Yucheng Zhou +5
Vision-based 3D semantic occupancy prediction is a critical task in 3D vision that integrates volumetric 3D reconstruction with semantic understanding. Existing methods, however, o…
Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs
Guangzong Si, Hao Yin, Xianfei Li +4
Multimodal Large Language Models (MLLMs) have achieved impressive advances, yet object hallucination remains a persistent challenge. Existing methods, based on the flawed assumptio…
Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction
Dubing Chen, Huan Zheng, Jin Fang +6
We present GDFusion, a temporal fusion method for vision-based 3D semantic occupancy prediction (VisionOcc). GDFusion opens up the underexplored aspects of temporal fusion within t…
You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Driving
Guangfeng Jiang, Jun Liu, Yongxuan Lv +5
Outdoor LiDAR point cloud 3D instance segmentation is a crucial task in autonomous driving. However, it requires laborious human efforts to annotate the point cloud for training a…
Generative Planning with 3D-vision Language Pre-training for End-to-End Autonomous Driving
Tengpeng Li, Hanli Wang, Xianfei Li +3
Autonomous driving is a challenging task that requires perceiving and understanding the surrounding environment for safe trajectory planning. While existing vision-based end-to-end…
Open-Vocabulary Object Detection via Neighboring Region Attention Alignment
Sunyuan Qiang, Xianfei Li, Yanyan Liang +3
The nature of diversity in real-world environments necessitates neural network models to expand from closed category settings to accommodate novel emerging categories. In this pape…
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
Xiaosong Jia, Shaoshuai Shi, Zijun Chen +4
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation. One natural solution is to estimate the pos…
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving
Han Lu, Xiaosong Jia, Yichen Xie +3
End-to-end differentiable learning for autonomous driving (AD) has recently become a prominent paradigm. One main bottleneck lies in its voracious appetite for high-quality labeled…
Grounding and Enhancing Grid-based Models for Neural Fields
Zelin Zhao, Fenglei Fan, Wenlong Liao +1
Many contemporary studies utilize grid-based models for neural field representation, but a systematic analysis of grid-based models is still missing, hindering the improvement of t…
MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving
Guangfeng Jiang, Jun Liu, Yuzhi Wu +3
Instance segmentation is a fundamental research in computer vision, especially in autonomous driving. However, manual mask annotation for instance segmentation is quite time-consum…
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
Xue Yang, Junchi Yan, Wenlong Liao +3
Small and cluttered objects are common in real-world which are challenging for detection. The difficulty is further pronounced when the objects are rotated, as traditional detector…