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Found 10 papers, 2 papers with code
SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
It involves Semantic-Guided Mask Labeling, which assigns semantic labels to unlabeled SAM masks using UDA pseudo-labels.
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings.
SUNet: Scale-aware Unified Network for Panoptic Segmentation
Panoptic segmentation combines the advantages of semantic and instance segmentation, which can provide both pixel-level and instance-level environmental perception information for intelligent vehicles.
Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation
In stage one, we design a threshold-adaptative unsupervised focal loss to regularize the prediction in the target domain, which has a mild gradient neutralization mechanism and mitigates the problem that hard samples are barely optimized in entropy-based methods.
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection
Thermal infrared (TIR) image has proven effectiveness in providing temperature cues to the RGB features for multispectral pedestrian detection.
Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios
As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios.
RFBNet: Deep Multimodal Networks with Residual Fusion Blocks for RGB-D Semantic Segmentation
To instantiate this structure, the paper proposes a residual fusion block (RFB) to formulate the interdependences of the encoders.
Ranked #3 on
Semantic Segmentation
on ScanNetV2
3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation
As a result, our framework can output both the semantic prediction and the instance prediction.
3D Instance Segmentation
3D Semantic Instance Segmentation
+2
Restricted Deformable Convolution based Road Scene Semantic Segmentation Using Surround View Cameras
Finally, an RDC based semantic segmentation model is built; the model is trained for real-world surround view images through a multi-task learning architecture by combining real-world images with transformed images.
Joint Calibration of Panoramic Camera and Lidar Based on Supervised Learning
In view of contemporary panoramic camera-laser scanner system, the traditional calibration method is not suitable for panoramic cameras whose imaging model is extremely nonlinear.
