Search Results for author:
Found 8 papers, 4 papers with code
3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning.
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic.
Structural Knowledge Distillation for Object Detection
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student.
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work.
The Stixel world: A medium-level representation of traffic scenes
We believe this challenge should be faced by introducing a representation of the sensory data that provides compressed and structured access to all relevant visual content of the scene.
Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling
Recent approaches for instance-aware semantic labeling have augmented convolutional neural networks (CNNs) with complex multi-task architectures or computationally expensive graphical models.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications.
A Discontinuous Neural Network for Non-Negative Sparse Approximation
It is shown that the presented network converges to equilibrium points which are solutions to general non-negative least squares optimization problems.
