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Found 8 papers, 4 papers with code
Uncertainty depth estimation with gated images for 3D reconstruction
Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence.
A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
Autonomous driving at level five does not only means self-driving in the sunshine.
Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving
Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions.
Learning Super-resolved Depth from Active Gated Imaging
Environment perception for autonomous driving is doomed by the trade-off between range-accuracy and resolution: current sensors that deliver very precise depth information are usually restricted to low resolution because of technology or cost limitations.
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios
This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available.
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs.
Ranked #2 on
2D Object Detection
on Clear Weather
Gated2Depth: Real-time Dense Lidar from Gated Images
The proposed replacement for scanning lidar systems is real-time, handles back-scatter and provides dense depth at long ranges.
On Deep Learning-Based Channel Decoding
We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes.
Information Theory Information Theory
