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Found 8 papers, 4 papers with code
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving
On the other hand, the generation of accurate control signals relies on precise and detailed environmental perception, which is where 3D scene perception models excel.
Multi-Resolution Fusion for Fully Automatic Cephalometric Landmark Detection
Cephalometric landmark detection on lateral skull X-ray images plays a crucial role in the diagnosis of certain dental diseases.
Decoupling the Curve Modeling and Pavement Regression for Lane Detection
The curve-based lane representation is a popular approach in many lane detection methods, as it allows for the representation of lanes as a whole object and maximizes the use of holistic information about the lanes.
Language Prompt for Autonomous Driving
A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt.
Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
To bridge this gap, we propose a new Direction-aware Cumulative Convolution Network (DaCCN), which improves the depth feature representation in two aspects.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency
When used as a pre-training method, our model can significantly outperform the corresponding fully-supervised baseline with only 1/3 3D labels.
Referring Multi-Object Tracking
In this paper, we propose a new and general referring understanding task, termed referring multi-object tracking (RMOT).
Learning to Fuse Asymmetric Feature Maps in Siamese Trackers
We propose a learnable module, called the asymmetric convolution (ACM), which learns to better capture the semantic correlation information in offline training on large-scale data.
Ranked #21 on
Visual Object Tracking
on TrackingNet
