Methods › Computer Vision › Motion Prediction Models › MotionNet

MotionNet

2 papers tagged archive 2025-07-28

Introduced by Pengxiang Wu et al. in MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

MotionNet is a system for joint perception and motion prediction based on a bird's eye view (BEV) map, which encodes the object category and motion information from 3D point clouds in each grid cell. MotionNet takes a sequence of LiDAR sweeps as input and outputs the bird's eye view (BEV) map. The backbone of MotionNet is a spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
3D Object Detection1
Autonomous Driving1
Object Detection1
motion prediction1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with MotionNet: 2020 to 2020, peak 2 2 0 2020: 2 papers 2020
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Motion Prediction Models

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