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Found 12 papers, 5 papers with code
Superpixel Transformers for Efficient Semantic Segmentation
Finally, we directly project the superpixel class predictions back into the pixel space using the associations between the superpixels and the image pixel features.
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection
Fusing camera and radar data is challenging, however, as each of the sensors lacks information along a perpendicular axis, that is, depth is unknown to camera and elevation is unknown to radar.
Instance Segmentation with Cross-Modal Consistency
We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities.
LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection
The popular object detection metric 3D Average Precision (3D AP) relies on the intersection over union between predicted bounding boxes and ground truth bounding boxes.
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving.
Block-NeRF: Scalable Large Scene Neural View Synthesis
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting
We propose GradTail, an algorithm that uses gradients to improve model performance on the fly in the face of long-tailed training data distributions.
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.
SoDA: Multi-Object Tracking with Soft Data Association
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars.
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
In such scenarios, the ability to accurately simulate the vehicle sensors such as cameras, lidar or radar is essential.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
