19 citations · 40 across the 12 of their papers we have counts for
6 papers · 1 filter
Correcting and Quantifying Systematic Errors in 3D Box Annotations for Autonomous Driving
Alexandre Justo Miro, Ludvig af Klinteberg, Bogdan Timus +5
Accurate ground truth annotations are critical to supervised learning and evaluating the performance of autonomous vehicle systems. These vehicles are typically equipped with activ…
DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance
Ajinkya Khoche, Qingwen Zhang, Yixi Cai +2
Accurate 3D scene flow estimation is critical for autonomous systems to navigate dynamic environments safely, but creating the necessary large-scale, manually annotated datasets re…
HiMo: High-Speed Objects Motion Compensation in Point Clouds
Qingwen Zhang, Ajinkya Khoche, Yi Yang +4
LiDAR point cloud is essential for autonomous vehicles, but motion distortions from dynamic objects degrade the data quality. While previous work has considered distortions caused…
SSF: Sparse Long-Range Scene Flow for Autonomous Driving
Ajinkya Khoche, Qingwen Zhang, Laura Pereira Sanchez +3
Scene flow enables an understanding of the motion characteristics of the environment in the 3D world. It gains particular significance in the long-range, where object-based percept…
Addressing Data Annotation Challenges in Multiple Sensors: A Solution for Scania Collected Datasets
Ajinkya Khoche, Aron Asefaw, Alejandro Gonzalez +3
Data annotation in autonomous vehicles is a critical step in the development of Deep Neural Network (DNN) based models or the performance evaluation of the perception system. This…
Towards Long-Range 3D Object Detection for Autonomous Vehicles
Ajinkya Khoche, Laura Pereira Sánchez, Nazre Batool +2
3D object detection at long range is crucial for ensuring the safety and efficiency of self driving vehicles, allowing them to accurately perceive and react to objects, obstacles,…