Conceitos essenciais
SpikeNeRFは、スパイクカメラからのニューラル放射輝度場学習を可能にする初の手法です。
Resumo
1. Abstract:
- Spike cameras offer advantages over standard cameras.
- SpikeNeRF derives a NeRF-based volumetric scene representation from spike camera data.
- Empirical evaluations affirm the efficacy of the methodology.
2. Introduction:
- Neuromorphic cameras have seen significant advancements.
- Event and spike cameras excel in capturing light intensity changes.
- NeRFs are explored for scene representation and novel view synthesis.
3. Methods:
- NeRF employs an MLP to learn a 3D volume representation.
- Spiking neuron layer and threshold variation simulation are used for spike sampling mechanism.
- Self-supervision with volumetric rendering is proposed.
4. Experiment:
- Synthetic and real-world data are used for evaluation.
- Quantitative results show the effectiveness of SpikeNeRF.
- Ablation studies confirm the importance of long-term spike rendering loss.
5. Conclusion:
SpikeNeRFは、スパイクカメラデータからの体積的シーン表現を可能にし、高品質な3D表現学習の研究に光を当てることを期待しています。
Estatísticas
スパイクカメラは空間分解能250×400と時間分解能20,000 Hzでリアルワールドのスパイクデータを記録します。