Spiking Neural Networks (SNNs), as a biologically inspired neural network architecture, have garnered significant attention due to their exceptional energy efficiency and increasing potential for various applications. In this work, we extend the use of SNNs to neural rendering tasks and introduce Spik-NeRF (Spiking Neural Radiance Fields). We observe that the binary spike activation map of traditional SNNs lacks sufficient information capacity, leading to information loss and a subsequent decline in the performance of spiking neural rendering models. To address this limitation, we propose the use of ternary spike neurons, which enhance the information-carrying capacity in the spiking neural rendering model. With ternary spike neurons, Spik-NeRF achieves performance that is on par with, or nearly identical to, traditional ANN-based rendering models. Additionally, we present a re-parameterization technique for inference that allows Spik-NeRF with ternary spike neurons to retain the event-driven, multiplication-free advantages typical of binary spike neurons. Furthermore, to further boost the performance of Spik-NeRF, we employ a distillation method, using an ANN-based NeRF to guide the training of our Spik-NeRF model, which is more compatible with the ternary neurons compared to the standard binary neurons. We evaluate Spik-NeRF on both realistic and synthetic scenes, and the experimental results demonstrate that Spik-NeRF achieves rendering performance comparable to ANN-based NeRF models.

Detecting Mars rocks on celestial bodies is crucial for the obstacle avoidance and path planning of space probes in deep space environments. However, the irregular shapes, background similarity, sparse pixel features, and susceptibility to lighting and dust effects make rock detection methods face significant problems in complex deep space environments. This challenge significantly impacts the accuracy and computational complexity of most rock detection methods. Therefore, this study develops a novel semantic segmentation network called EDR-TransUnet for rock image segmentation on the surface of Mars. The proposed network uses the enhanced dual relation (EDR)-attention block, heterogeneous kernel convolution (HetConv), and TransUnet hybrid framework. By integrating global and local features, image-specific positional and channel features, and using the Transformer and EDR-Block, the performance of rock image segmentation on Mars is improved. The EDR-TransUnet significantly enhances the multiscale rock feature extraction capability and improves the efficiency of the encoder-decoder structure by incorporating EDR blocks in the embedding layer and each skip-connection layer. Additionally, introducing HetConv reduces the model’s computational complexity and improves its efficiency. Experiments on our newly developed ZhurongRock Mars rock dataset, the publicly available MarsData-V2 database and the synthetic TianWen-1 database (SynMars) demonstrated that EDR-TransUnet surpasses the state-of-the-art rock detection algorithms while maintaining lower computational complexity and faster inference speed. For example, the pixel accuracy (PA) and precision on the ZhurongRock dataset reached 97.82% and 96.18%, respectively. The codes and data are available athttps://github.com/JYTCV/EDR-TransUnet.