Andrei Pokrovsky

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Andrei Pokrovsky is a researcher/engineer at Uber Advanced Technologies Group Toronto.

Engineering Blog Articles

SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.

Research Papers

Learning to Localize Using a LiDAR Intensity Map

I. Bârsan, S. Wang, A. Pokrovsky, R. Urtasun
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. [...] [PDF]
Conference on Robot Learning (CORL), 2018

Deep Parametric Continuous Convolutional Neural Networks

S. Wang, S. Suo, W. Ma, A. PokrovskyR. Urtasun
We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

SBNet: Sparse Block’s Network for Fast Inference

M. Ren, A. Pokrovsky, B. Yang, R. Urtasun
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications. For many problems such as object detection and semantic segmentation, we are able to obtain a low-cost computation mask, either from a priori problem knowledge, or from a low-resolution segmentation network. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018

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