127 citations · 315 across the 22 of their papers we have counts for
22 papers
ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation
Boni Hu, Xiong Wei, Haoming Huang +20
Generative world models offer controllable and repeatable closed-loop simulation for end-to-end and vision-language-action driving policies, but production deployment exposes three…
Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs
Zixuan Huang, Yang Zhou, Kaixuan Wang +7
Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision…
SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them
Yang Zhou, Zixuan Huang, Sunzhu Li +10
Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that r…
Toward Deep Representation Learning for Event-Enhanced Visual Autonomous Perception: the eAP Dataset
Jinghang Li, Shichao Li, Qing Lian +3
Recent visual autonomous perception systems achieve remarkable performances with deep representation learning. However, they fail in scenarios with challenging illumination.While e…
Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
Shichao Li, Peiliang Li, Qing Lian +2
Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for su…
Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving
JunDa Cheng, Wei Yin, Kaixuan Wang +3
Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are…
GIM: Learning Generalizable Image Matcher From Internet Videos
Xuelun Shen, Zhipeng Cai, Wei Yin +5
Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-…
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
Kai Cheng, Xiaoxiao Long, Wei Yin +6
Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities. Despite the fast development of Neural…
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
Wei Yin, Chi Zhang, Hao Chen +5
Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-posedness of the single-image reconstruction problem, most well-established methods are…
The Second Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Michaela Trescakova +40
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, includin…
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
Rui Li, Dong Gong, Wei Yin +6
Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consis…
Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion
Jieqi Shi, Peiliang Li, Xiaozhi Chen +1
In the practical application of point cloud completion tasks, real data quality is usually much worse than the CAD datasets used for training. A small amount of noisy data will usu…
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning
Jieqi Shi, Peiliang Li, Xiaozhi Chen +1
The image-based 3D object detection task expects that the predicted 3D bounding box has a ``tightness'' projection (also referred to as cuboid), which fits the object contour well…
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
Jialei Xu, Xianming Liu, Yuanchao Bai +4
Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estima…
UniFusion: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View
Zequn Qin, Jingyu Chen, Chao Chen +2
Bird's eye view (BEV) representation is a new perception formulation for autonomous driving, which is based on spatial fusion. Further, temporal fusion is also introduced in BEV re…
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
Qing Lian, Peiliang Li, Xiaozhi Chen
Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object detection lacks accurate depth recovery ability. Although the deep neural network (DNN) enables monocu…
Temporal Point Cloud Completion with Pose Disturbance
Jieqi Shi, Lingyun Xu, Peiliang Li +2
Point clouds collected by real-world sensors are always unaligned and sparse, which makes it hard to reconstruct the complete shape of object from a single frame of data. In this w…
Geometry-based Distance Decomposition for Monocular 3D Object Detection
Xuepeng Shi, Qi Ye, Xiaozhi Chen +3
Monocular 3D object detection is of great significance for autonomous driving but remains challenging. The core challenge is to predict the distance of objects in the absence of ex…
Stereo R-CNN based 3D Object Detection for Autonomous Driving
Peiliang Li, Xiaozhi Chen, Shaojie Shen
We propose a 3D object detection method for autonomous driving by fully exploiting the sparse and dense, semantic and geometry information in stereo imagery. Our method, called Ste…
Multi-View 3D Object Detection Network for Autonomous Driving
Xiaozhi Chen, Huimin Ma, Ji Wan +2
This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR poin…
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
Xiang Wang, Huimin Ma, Xiaozhi Chen +1
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of…
3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
Xiaozhi Chen, Kaustav Kundu, Yukun Zhu +3
The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method first aims at generating a set of high-quality 3D object proposals by expl…