9 papers
ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning
Zijie Wang, Wei Zhang, Weiming Zhang +4
Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE). However, they implicitly assume that depth can be recovered as a globally smooth f…
World Models as Group Actions
Zijie Wang, Wei Zhang, Weiming Zhang +4
Video world models have achieved strong visual realism, but this does not ensure that their dynamics are truly governed by actions. In this work, we argue that action faithfulness…
Thinking While Driving: A Concurrent Framework for Real-Time, LLM-Based Adaptive Routing
Xiaopei Tan, Muyang Fan
We present Thinking While Driving, a concurrent routing framework that integrates LLMs into a graph-based traffic environment. Unlike approaches that require agents to stop and del…
CoT4Det: A Chain-of-Thought Framework for Perception-Oriented Vision-Language Tasks
Yu Qi, Yumeng Zhang, Chenting Gong +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable success in a broad range of vision-language tasks, such as general visual question answering and optical character…
Fin3R: Fine-tuning Feed-forward 3D Reconstruction Models via Monocular Knowledge Distillation
Weining Ren, Hongjun Wang, Xiao Tan +1
We present Fin3R, a simple, effective, and general fine-tuning method for feed-forward 3D reconstruction models. The family of feed-forward reconstruction model regresses pointmap…
LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation
Zijie Wang, Weiming Zhang, Wei Zhang +4
Centerline graphs, crucial for path planning in autonomous driving, are traditionally learned using deterministic methods. However, these methods often lack spatial reasoning and s…
VLDrive: Vision-Augmented Lightweight MLLMs for Efficient Language-grounded Autonomous Driving
Ruifei Zhang, Wei Zhang, Xiao Tan +4
Recent advancements in language-grounded autonomous driving have been significantly promoted by the sophisticated cognition and reasoning capabilities of large language models (LLM…
AdaDrive: Self-Adaptive Slow-Fast System for Language-Grounded Autonomous Driving
Ruifei Zhang, Junlin Xie, Wei Zhang +4
Effectively integrating Large Language Models (LLMs) into autonomous driving requires a balance between leveraging high-level reasoning and maintaining real-time efficiency. Existi…
HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking
Jing Yang, Sen Yang, Xiao Tan +1
As an essential component of autonomous driving systems, high-definition (HD) maps provide rich and precise environmental information for auto-driving scenarios; however, existing…