Summary of Efficient Depth Estimation For Unstable Stereo Camera Systems on Ar Glasses, by Yongfan Liu et al.
Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses
by Yongfan Liu, Hyoukjun Kwon
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposes two new models for stereo depth estimation that can be used in augmented reality (AR) applications. Traditional depth estimation models often require time-consuming preprocessing steps, such as rectification, to achieve high accuracy. However, these preprocessing steps can add significant latency, making them unsuitable for real-time AR applications on mobile platforms. The authors develop two new models, MultiHeadDepth and HomoDepth, that eliminate the need for preprocessing and provide low latency while maintaining high accuracy. The MultiHeadDepth model replaces traditional cost volume operators with a group-pointwise convolution-based operator and approximates cosine similarity using layernorm and dot product. The HomoDepth model adds rectification positional encoding to predict homography matrices, allowing it to process unrectified images and reduce end-to-end latency by 44.5%. The authors also adopt a multi-task learning framework for handling misaligned stereo inputs on HomoDepth, reducing the AbsRel error by 10.0-24.3%. Experimental results show that both models provide significant improvements in accuracy and latency compared to state-of-the-art depth estimation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making augmented reality (AR) more real-time by improving how we estimate distances between objects. Right now, most AR systems use old-fashioned ways to calculate these distances, which makes them slow and not suitable for everyday use. The authors of this paper developed two new methods that can do this calculation much faster and with better accuracy than before. One method uses a special type of convolutional neural network (CNN) called a group-pointwise CNN, while the other method uses a combination of CNNs and special encoding to predict how images should be aligned. This means we can use AR in more situations without waiting for ages for it to load or calculate distances. |
Keywords
» Artificial intelligence » Cnn » Cosine similarity » Depth estimation » Dot product » Multi task » Neural network » Positional encoding
