@article{khoche2025ssf,title={SSF: Sparse Long-Range Scene Flow for Autonomous Driving},author={Khoche, Ajinkya and Zhang, Qingwen and Sanchez, Laura Pereira and Asefaw, Aron and Mansouri, Sina Sharif and Jensfelt, Patric},journal={arXiv preprint arXiv:2501.17821},year={2025},}
2024
ECC 2024
Addressing Data Annotation Challenges in Multiple Sensors: A Solution for Scania Collected Datasets
Ajinkya Khoche, Aron Asefaw, Alejandro González, and 3 more authors
@inproceedings{khoche2024addressing,title={Addressing Data Annotation Challenges in Multiple Sensors: A Solution for Scania Collected Datasets},author={Khoche, Ajinkya and Asefaw, Aron and Gonz{\'a}lez, Alejandro and Timus, Bogdan and Mansouri, Sina Sharif and Jensfelt, Patric},booktitle={2024 European Control Conference (ECC)},pages={1032--1038},year={2024},organization={IEEE},}
IV 2024
Towards long-range 3d object detection for autonomous vehicles
Ajinkya Khoche, Laura Pereira Sánchez, Nazre Batool, and 2 more authors
In 2024 IEEE Intelligent Vehicles Symposium (IV), 2024
@inproceedings{khoche2024towards,title={Towards long-range 3d object detection for autonomous vehicles},author={Khoche, Ajinkya and S{\'a}nchez, Laura Pereira and Batool, Nazre and Mansouri, Sina Sharif and Jensfelt, Patric},booktitle={2024 IEEE Intelligent Vehicles Symposium (IV)},pages={2206--2212},year={2024},organization={IEEE},}
2022
IV 2022
Semantic 3d grid maps for autonomous driving
Ajinkya Khoche, Maciej K Wozniak, Daniel Duberg, and 1 more author
In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022
@inproceedings{khoche2022semantic,title={Semantic 3d grid maps for autonomous driving},author={Khoche, Ajinkya and Wozniak, Maciej K and Duberg, Daniel and Jensfelt, Patric},booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)},pages={2681--2688},year={2022},organization={IEEE},}
2020
M.Sc. Thesis
Depth Estimation from Images using Dense Camera-Lidar Correspondences and Deep Learning
Ajinkya Khoche
KTH, School of Electrical Engineering and Computer Science (EECS), 2020
Depth estimation from 2D images is a fundamental problem in Computer Vision, and is increasingly becoming an important topic for Autonomous Driving. A lot of research is driven by innovations in Convolutional Neural Networks, which efficiently encode low as well as high level image features and are able to fuse them to find accurate pixel correspondences and learn the scale of the objects. Current state-of-the-art deep learning models employ a semi-supervised learning approach, which is a combination of unsupervised and supervised learning. Most of the research community relies on the KITTI datasets for benchmarking of results. But the training performance is known to be limited by the sparseness of the lidar ground truth as well as lack of training data. In this thesis, multiple stereo datasets with increasingly denser depth maps are generated on the corpus of driving data collected at the Audi Electronics Venture GmbH. In this regard, a methodology is presented to obtain an accurate and dense registration between the camera and lidar sensors. Approaches are also outlined to rectify the stereo image datasets and filter the depth maps. Keeping the architecture fixed, a monocular and a stereo depth estimation network each are trained on these datasets and their performances are compared to other networks reported in literature. The results are competitive, with the stereo network exceeding the state-of-the-art accuracy. More work is needed though to establish the influence of increasing depth density on depth estimation performance. The proposed method forms a solid platform for pushing the envelope of depth estimation research as well as other application areas critical to autonomous driving.
@mastersthesis{Khoche1417257,author={Khoche, Ajinkya},institution={KTH, School of Electrical Engineering and Computer Science (EECS)},school={KTH, School of Electrical Engineering and Computer Science (EECS)},title={Depth Estimation from Images using Dense Camera-Lidar Correspondences and Deep Learning},series={TRITA-EECS-EX},number={2020:12},year={2020},}
