Summary of Fast Occupancy Network, by Mingjie Lu et al.
Fast Occupancy Network
by Mingjie Lu, Yuanxian Huang, Ji Liu, Xingliang Huang, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum
First submitted to arxiv on: 10 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 paper proposes a novel approach to occupancy prediction in autonomous driving, transforming the 3D detection task into a 3D voxel segmentation problem. The Occupancy Network model predicts the category of voxels in a specified 3D space around the ego vehicle, providing fine-grained 3D representations and tackling category outlier obstacles. However, existing methods require significant computational resources, hindering their application in intelligent driving systems. To address this issue, the authors analyze the bottleneck of Occupancy Network inference cost and present a simple and fast model that leverages deformable 2D convolutional layers to lift BEV features to 3D voxel features and an efficient voxel feature pyramid network (FPN) module to improve performance with minimal computational cost. The method also incorporates a cost-free 2D segmentation branch in perspective view during the inference phase, enhancing accuracy. Experimental results demonstrate that the proposed approach outperforms existing methods in terms of both accuracy and inference speed, achieving a 1.7% improvement over recent state-of-the-art OCCNet with ResNet50 backbone while reducing inference time by approximately threefold. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to predict where objects are in space using computers. This can help self-driving cars see the world more clearly and avoid accidents. Instead of just detecting objects, this method tries to figure out what type of object it is, like a car or a pedestrian. It does this by looking at small cubes (called voxels) in 3D space and saying what’s inside each one. This helps with tricky situations where there might be objects that are hard to detect just using cameras. The problem is that this method uses a lot of computer power, which can make it hard to use in real-life self-driving cars. To fix this, the authors came up with a way to speed things up while still keeping good accuracy. They tested their approach and found that it worked better than other methods at predicting where objects are. |
Keywords
» Artificial intelligence » Feature pyramid » Inference
