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Found 12 papers, 5 papers with code
AGO: Adaptive Grounding for Open World 3D Occupancy Prediction
Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects.
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners
To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction.
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving
State-of-the-art approaches for autonomous driving integrate multiple sub-tasks of the overall driving task into a single pipeline that can be trained in an end-to-end fashion by passing latent representations between the different modules.
ADA-Track++: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association
Tracking-by-attention, however, entangles detection and tracking queries in one embedding for both the detection and tracking task, which is sub-optimal.
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.
