Summary of 3d Single-object Tracking in Point Clouds with High Temporal Variation, by Qiao Wu et al.
3D Single-object Tracking in Point Clouds with High Temporal Variation
by Qiao Wu, Kun Sun, Pei An, Mathieu Salzmann, Yanning Zhang, Jiaqi Yang
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework called HVTrack for 3D single-object tracking (3D SOT) in point clouds with high temporal variation. The current approaches assume smooth shape variation and object motion, but HVTrack tackles this challenge by introducing three novel components: Relative-Pose-Aware Memory, Base-Expansion Feature Cross-Attention, and Contextual Point Guided Self-Attention. These modules enable the tracker to handle temporal shape variations, similar object distractions, and heavy background noise. The paper also constructs a high-temporal-variation dataset (KITTI-HV) and compares HVTrack with state-of-the-art tracker CXTracker on this dataset, achieving 11.3% and 15.7% improvements in Success and Precision respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a better way to track objects in 3D point clouds when there’s lots of variation over time. Right now, most methods assume the object’s shape doesn’t change much between frames or that it moves smoothly from one frame to the next. But this new approach called HVTrack does things differently by using three special modules to handle big changes in the object’s shape and position. This helps the tracker ignore distractions and stay focused on the right object. The researchers even created a special dataset (KITTI-HV) with lots of variation and tested their method against another popular one, getting better results. |
Keywords
* Artificial intelligence * Cross attention * Object tracking * Precision * Self attention
