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Found 14 papers, 4 papers with code
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving
Reinforcement Learning (RL) has the potential to surpass human performance in driving without needing any expert supervision.
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes
In this work, we extend beyond identifying OoD road obstacles in video streams and offer a comprehensive approach to extract sequences of OoD road obstacles using text queries, thereby proposing a way of curating a collection of OoD data for subsequent analysis.
ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation
To address this challenge, we propose ADAPT, a novel approach for jointly predicting the trajectories of all agents in the scene with dynamic weight learning.
Multi-Object Discovery by Low-Dimensional Object Motion
We achieve state-of-the-art results in unsupervised multi-object segmentation on synthetic and real-world datasets by modeling the scene structure and object motion.
DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and Parallax
We perform experiments on the KITTI driving dataset and show that the planar parallax approach, which only needs to predict camera translation, can be a metrically accurate alternative to the current methods that rely on estimating 6DoF camera motion.
RbA: Segmenting Unknown Regions Rejected by All
Our extensive experiments show that mask classification improves the performance of the existing outlier detection methods, and the best results are achieved with the proposed RbA.
Ranked #1 on
Anomaly Detection
on Road Anomaly
(using extra training data)
Two-Level Temporal Relation Model for Online Video Instance Segmentation
In Video Instance Segmentation (VIS), current approaches either focus on the quality of the results, by taking the whole video as input and processing it offline; or on speed, by handling it frame by frame at the cost of competitive performance.
Trajectory Forecasting on Temporal Graphs
We complement our representation with two types of memory modules; one focusing on the agent of interest and the other on the entire scene.
Ranked #51 on
Motion Forecasting
on Argoverse CVPR 2020
StretchBEV: Stretching Future Instance Prediction Spatially and Temporally
Our model learns temporal dynamics in a latent space through stochastic residual updates at each time step.
Stochastic Video Prediction with Structure and Motion
The existing methods fail to fully capture the dynamics of the structured world by only focusing on changes in pixels.
Frustum Fusion: Pseudo-LiDAR and LiDAR Fusion for 3D Detection
Most autonomous vehicles are equipped with LiDAR sensors and stereo cameras.
Self-Supervised Monocular Scene Decomposition and Depth Estimation
Self-supervised monocular depth estimation approaches either ignore independently moving objects in the scene or need a separate segmentation step to identify them.
SLAMP: Stochastic Latent Appearance and Motion Prediction
Motion is an important cue for video prediction and often utilized by separating video content into static and dynamic components.
Ranked #1 on
Video Prediction
on Cityscapes 128x128
(PSNR metric)
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes.
