funded by Ministry of Trade, Industry and Energy of Korea. (MOTIE)(No.10044775)
- Research trend of sensor fusion for object detection
- State-of-the-art
- 3D Object Proposals for Accurate Object Class Detection [Chen, NIPS 15] [Project] [Code]
- Multiview random forest of local experts combining rgb and lidar data for pedestrian detection [Gonzalez, IV '15]
- Voting for Voting in Online Point Cloud Object Detection [Wang, RSS '15] [Project]
- Pedestrian Detection Combining RGB and Dense LIDAR Data [Premebida, IROS '14] [Project] [Code]
- Vehicle Detection from 3D Lidar Using Fully Convolutional Network [Li, RSS '16]
- Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes [Yebes, Sensors '15]
- Sensor configuration of autonomous vehicles
- Tesla
- BMW
- State-of-the-art
- Build a testbed / Select datasets
- Datasets: Traffic scenes
- KITTI [Link]
- Stereo, Lidar, GPS
- Classes: Car, Pedestrian, Cyclist
- GT: Bounding box
- Cityscapes [Link]
- Stereo, Timestamp
- Groups: flat, human, vehicle, construction, object, nature, sky, void
- GT: Dense pixel-level annotations
- Virtual KITTI [Link]
- Mono (forward / 15-deg-right, 15-deg-left)
- Classes: Car, Pedestrian, Cyclist
- GT: Bounding box, Instance-level pixel annotations, Optical-flow, Depth
- Weather conditions: morning, sunset, overcast, fog, rain
- Synthia [Link]
- 8 RGB (form binocular 360 deg), 8 depth sensors
- Classes: misc, sky, building, road, sidewalk, fence, vegetation, pole, car, sign, pedestrian, cyclist, lanemarking
- GT: Instance-level pixel annotations
- Seasons: winter, fall, spring, summer
- Lightings: dynamic light, shadows, day-time, rain, night-time
- KITTI [Link]
- Datasets: Traffic scenes
- Implement several algorithms
- Evaluation / Comparison / Analysis
- Unsupervised Depth Estimation. [Garg, ECCV '16]
- LIDAR point upsampling. [Schneider, Arxiv '16]
- Unified multi-scale CNN. (KITTI: 8th car, 1st ped) [Cai, ECCV '16] [Home] [Code] [Video]
- Subcategory-aware CNN. (KITTI: 7th car, 3rd ped)) [Xiang, Arxiv '16] [Home]
- Exploit all layers. (KITTI: 10th car, 5th ped) [Yang, CVPR '16] [Home]
- 2D/3D Sensor Exploitation and Fusion for Enhanced Object Detection (Similar to ours) [Xu, CVPRW '14]