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Found 5 papers, 4 papers with code
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning
The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning.
APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds
To leverage the different properties of each branch, we employ a geometry-aware fusion module that is learned to combine the results of each branch.
Objects do not disappear: Video object detection by single-frame object location anticipation
2) Improved efficiency by only doing the expensive feature computations on a small subset of all frames.
Ranked #1 on
Video Object Detection
on Waymo Open Dataset
No frame left behind: Full Video Action Recognition
A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action.
KPRNet: Improving projection-based LiDAR semantic segmentation
Semantic segmentation is an important component in the perception systems of autonomous vehicles.
Ranked #15 on
3D Semantic Segmentation
on SemanticKITTI
