3 papers
cs.CV2026
HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training
Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan +4
Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense…
cs.CV2026
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…
cs.CV2025
BlendCLIP: Bridging Synthetic and Real Domains for Zero-Shot 3D Object Classification with Multimodal Pretraining
Ajinkya Khoche, GergŠLászló Nagy, Maciej Wozniak +2
Zero-shot 3D object classification is crucial for real-world applications like autonomous driving, however it is often hindered by a significant domain gap between the synthetic da…