Summary of Towards Flexible 3d Perception: Object-centric Occupancy Completion Augments 3d Object Detection, by Chaoda Zheng et al.
Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection
by Chaoda Zheng, Feng Wang, Naiyan Wang, Shuguang Cui, Zhen Li
First submitted to arxiv on: 6 Dec 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 The proposed paper introduces a novel representation called object-centric occupancy, which supplements traditional 3D bounding boxes (bboxes) to capture detailed intrinsic geometry of foreground objects. This approach enables higher voxel resolution in large scenes while improving detection results for incomplete or distant objects. The authors construct the first dataset from scratch and introduce an occupancy completion network with an implicit shape decoder that leverages temporal information from long sequences. Experimental results demonstrate robust performance under noisy conditions and showcase significant enhancements to state-of-the-art 3D object detectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way of understanding objects in 3D scenes, called object-centric occupancy. Instead of just seeing the box around an object, this approach shows us the details inside the box. It’s like going from a simple outline drawing to a detailed painting. The authors create a new dataset and a special algorithm that can fill in the missing parts of the object’s shape. This helps 3D object detectors become better at finding objects even if they are incomplete or far away. |
Keywords
» Artificial intelligence » Decoder
