Dense Voxel Fusion for 3D Object Detection

Camera and LiDAR sensor modalities provide complementary appearance and geometric information useful for detecting 3D objects for autonomous vehicle applications. However, current end-to-end fusion methods are challenging to train and underperform state-of-the-art LiDAR-only detectors. Sequential fusion methods suffer from a limited number of pixel and point correspondences due to point cloud sparsity, or their performance is strictly capped by the detections of one of the modalities. Our proposed solution, Dense Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations, improving expressiveness in low point density regions. To enhance multi-modal learning, we train directly with projected ground truth 3D bounding box labels, avoiding noisy, detector-specific 2D predictions. Both DVF and the multi-modal training approach can be applied to any voxel-based LiDAR backbone. DVF ranks 3rd among published fusion methods on KITTI 3D car detection benchmark without introducing additional trainable parameters, nor requiring stereo images or dense depth labels. In addition, DVF significantly improves 3D vehicle detection performance of voxel-based methods on the Waymo Open Dataset.Comment: Accepted in WACV 202

Model-aided state estimation for quadrotor micro air vehicles amidst wind disturbances

© 2014 IEEE. This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims

Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss

An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes. We propose to alleviate the self-similarity problem through a novel semantically tolerant image-to-point contrastive loss that takes into consideration the semantic distance between positive and negative image regions to minimize contrasting semantically similar point and image regions. Additionally, we address class imbalance by designing a class-agnostic balanced loss that approximates the degree of class imbalance through an aggregate sample-to-samples semantic similarity measure. We demonstrate that our semantically-tolerant contrastive loss with class balancing improves state-of-the art 2D-to-3D representation learning in all evaluation settings on 3D semantic segmentation. Our method consistently outperforms state-of-the-art 2D-to-3D representation learning frameworks across a wide range of 2D self-supervised pretrained models.Comment: Accepted in CVPR 202

La estrategia Educativa 2020 o las limitaciones del Banco Mundial para promover el "aprendizaje para todos"

La nueva Estrategia Educativa 2020 del Banco Mundial establece las prioridades de reforma educativa en paises en vias de desarrollo para la decada siguiente. El titulo explicito de la estrategia, Aprendizaje para Todos, es un claro reconocimiento de que, mas alla de politicas centradas en el acceso, se debe hacer algo mas para asegurar que la educacion derive en experiencias positivas de aprendizaje. Sin embargo, como este articulo sostiene, las opciones de politicas explicitas y latentes en la Estrategia 2020 no son las mas adecuadas para lograr el Aprendizaje para Todos. El articulo desarrolla tres tipos de argumentos al respecto. El primero se refiere al fuerte apego del Banco a un conocimiento disciplinario y un enfoque metodológico que es insufi ciente para entender lo que aprenden los niños en la escuela y por que. El segundo argumento se refiere al sesgo pro-mercado de la Estrategia por lo que respecta a la reforma del sector publico y a nuevas formas de oferta educativa. En tercer lugar, el articulo senala las principales ausencias de la Estrategia, con especial atencion a las omisiones relacionadas con la compleja relación entre educación y pobreza.The World Bank's 2020 Education Strategy establishes the new education priorities in developing countries for the next decade. Its title, Learning for All, clearly recognizes that, beyond policies focusing on access, something else must be done to ensure that schooling involves positive learning experiences. However, as this paper argues, the 2020 Strategy explicit and latent policy options might not be adequate to achieve Learning for All. This paper develops three arguments on that matter. The fi rst one refers to the Bank's strong attachment to a disciplinary knowledge and a methodological approach that do not suffi ce to understand what children learn at school and why. The second one addresses its pro-market bias when it approaches the public sector reforms and the new forms of providing education. The last argument points out the main omissions of this Strategy, especially in what regards the complex relation between education and poverty