430 citations · 1.2k across the 77 of their papers we have counts for
10 papers · 1 filter
Convolutional Recurrent Network for Road Boundary Extraction
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma +2
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper…
Deep Continuous Fusion for Multi-Sensor 3D Object Detection
Ming Liang, Bin Yang, Shenlong Wang +1
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-en…
Learning to Localize Through Compressed Binary Maps
Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang +2
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compr…
Learning to Localize Using a LiDAR Intensity Map
Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky +1
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity…
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
Julieta Martinez, Sasha Doubov, Jack Fan +4
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M,…
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Sourav Biswas, Jerry Liu, Kelvin Wong +2
We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to red…