BingのAIチャットを使った論文要約

# 概要

昨年IROS2022の論文まとめを人力で行いましたが、最近はBingのAIチャット機能を使ってまとめをしています。ここでは私が使っているプロンプトとその結果得られる文について紹介します。英語論文を読むのが苦手な人の助けになればと思います。

# プロンプト

論文を理解するために必要な情報を整理した結果以下のような感じになりました。
* 方針
    * 要約文は自分にとって必要な情報が反映されるかどうかを重視する。多少長くなってもよい。
    * 英語に慣れるために要約文を日本語にしない。
        * 出力の高速化にもつながる。

```text
Please summarize the content of the papers listed on this page in the following structure.
# Restrictions
- Please use the following format for your summary.
* Abstract
* Background
* Motivation
* Proposed Methodology
* Novelty
* Related works
* Experimental Setup
* Evaluation Methodology
* Comparison
* Evaluation results
* Conclusion
* Future work
- In the summary, use words from the paper whenever possible.
- In the proposed methodology section, be sure to include important key words.
- Describe any studies that have had a significant impact on the present study.
- Be sure to describe the strengths and weaknesses of the proposed methodology in the evaluation results, and provide numerical comparisons whenever possible
```

# 例

以下の論文の場合、どのように訳されるか見てみます。
※2023年6月時点では、会話のスタイルを「**より厳密に**」したほうが本文との差異がすくなるなるはずです。

[VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://arxiv.org/pdf/2302.12251.pdf)

Here is a summary of the paper “VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion” based on the current web page context:

* **Abstract**: This paper proposes VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images. The framework adopts a two-stage design where it starts from a sparse set of visible and occupied voxel queries from depth estimation, followed by a densification stage that generates dense 3D voxels from the sparse ones. Experiments on SemanticKITTI show that VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory during training to less than 16GB.

* **Background**: Holistic 3D scene understanding is an important problem in autonomous vehicle (AV) perception. It directly affects downstream tasks such as planning and map construction.

* **Motivation**: Obtaining accurate and complete 3D information of the real world is difficult, since the task is challenged by the lack of sensing resolution and the incomplete observation due to the limited field of view and occlusions.

* **Proposed Methodology**: The proposed methodology is a two-stage framework that lifts images into a complete 3D voxelized semantic scene. Stage-1 has a lightweight 2D CNN-based query proposal network using the image depth to reconstruct the scene geometry. Stage-2 is based on a novel sparse-to-dense MAE-like architecture. It first strengthens the featurization of the proposed voxels by allowing them to attend to the image observations. Next, the non-proposed voxels will be associated with a learnable mask token, and the full set of voxels will be processed by self-attention to complete the scene representations for per-voxel semantic segmentation.

* **Novelty**: The key idea of this design is that the visual features on 2D images correspond only to the visible scene structures rather than the occluded or empty spaces. Therefore, starting with the featurization and prediction of the visible structures is more reliable.

* **Related works**: Most existing SSC solutions consider LiDAR as a primary modality to enable accurate 3D geometric measurement. However, LiDAR sensors are expensive and less portable, while cameras are cheaper and provide richer visual cues of the driving scenes. This motivated the study of camera-based SSC solutions, as first proposed in MonoScene.

* **Experimental Setup**: The authors verify VoxFormer on SemanticKITTI, which provides dense semantic annotations for each LiDAR sweep from KITTI Odometry Benchmark composed of 22 outdoor driving scenarios.

* **Evaluation Methodology**: The authors employ intersection over union (IoU) to evaluate scene completion quality, regardless of allocated semantic labels. They also use mean IoU (mIoU) of 19 semantic classes to assess performance of semantic segmentation.

* **Comparison**: VoxFormer is compared against state-of-the-art SSC methods with public resources: (1) a camera-based SSC method MonoScene based on 2D-to-3D feature projection, (2) LiDAR-based SSC methods including JS3CNet, LMSCNet, and SSCNet, and (3) RGB-inferred baselines LMSCNet∗ and SSCNet∗ which take as input a pseudo LiDAR point cloud generated by stereo depth.

* **Evaluation results**:
    * **Pros**: VoxFormer outperforms MonoScene by a large margin in terms of geometric completion (36.80 → 44.02, 19.62%). VoxFormer even performs on par with some LiDAR-based methods at close range.
    * **Cons**: None mentioned.

* **Conclusion**: VoxFormer sets a new state-of-the-art in camera-based SSC on SemanticKITTI.

* **Future work**: None mentioned.

# まとめ

要約と本文を行き来する中で、欲しい情報が反映されないようならプロンプトを自分なりに更新することをおすすめします。