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Learning Lane Graph Representations for Motion Forecasting

\[ECCV 2020 Oral\] We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we construct a lane graph from raw map data to …

Dsdnet: Deep structured self-driving network

\[ECCV 2020\] In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. Towards this goal, we develop a deep structured energy …

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

\[CVPR 2020\]We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as input sequential sensor data, and outputs at each time step …

Learning Joint 2D-3D Representations for Depth Completion

\[ICCV 2019\] Depth completion using 2D-3D fusion. New SOTA achieved in KITTI.

Multi-task multi-sensor fusion for 3d object detection

\[CVPR 2019\] In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground …

Volume R-CNN: Unified Framework for CT Object Detection and Instance Segmentation

\[ISBI 2019\] Accurate and Efficient 3D Nodule detection with 3D R-CNN.