## 4 Results and Evaluation
### Quality Evaluation Experiments  
Our evaluation experiments were designed to test the method's quality in three scenarios: without using an update strategy, with a single-step update, and with a five-step update process. Detailed criteria defined each update step's scope and impact on the model's performance. This approach was designed to validate the effectiveness of the multiagent update strategy. Through these tests, we compared our method against other leading-edge techniques, including DreamFusion, PureClipNeRF, and DreamGaussian, utilizing the CLIP models from OpenAI for our evaluations. Figure 6 shows some of our experimental results on multiple datasets. In the easy dataset, our no-update method outperformed PureClipNeRF and DreamGaussian by 0.2 and showed a notably larger advantage over DreamFusion by 0.6, as quantified by the adjusted scores in Table 1. This can be attributed to the smaller volume of input text and the relatively vague requirements for terrain generation in this dataset, where the unique spatial perception capabilities of our method were not fully leveraged. However, even with minimal textual input, our approach yielded higher-quality generations than those based on diffusion models. \begin{table} \begin{tabular}{c c c} \hline \hline
**Method** & **Regular Score** & **Adjusted Score 1** \\ \hline DreamFusion & 3.54 & 3.63 \\ PureClipNeRF & 3.84 & 3.82 \\ DreamGaussian & 4.01 & 4.03 \\ Ours (without modify) & 4.15 & 4.17 \\ Ours (with 1 step modify) & 4.23 & 4.26 \\ Ours (with 5 steps modify) & 4.52 & 4.69 \\ \hline \hline \end{tabular}  1  
Adjusted score is calculated by removing the highest and lowest scores before averaging. We believe that short texts do not provide sufficient spatial information. Large language models can establish a mapping from short to extended texts, thus enriching the text with adequate spatial information. In contrast, diffusion models, which learn from two-dimensional images, struggle to grasp the association between spatial and textual information and cannot directly supplement more information in the form of extended text. Our method's superior performance on the medium and hard datasets, with an improvement of 0.99 over the closest competitor, can be attributed to these datasets' complexity, as shown in Tables 2 and 3:  
Figure 6: We converted NERF and Gaussian results into mesh objects using methods from existing research [46; 47]. This conversion facilitates a more effective comparison with our method in practical scenarios. It is observable that, compared with other methods based on diffusion models, our approach can leverage the language recognition capabilities of large language models to make accurate judgments about spatial information and generate precise three-dimensional terrains that align with the descriptions of location information. Additionally, our method employs heightmaps for construction, which significantly reduces the number of tiles required for the three-dimensional terrain, further enhancing the practical value of our approach. This complexity better showcases our approach's spatial perception capabilities, particularly in basin scenarios in Figure 1. The medium dataset introduced descriptions of spatial and orientation information, challenging for diffusion models due to their difficulty in maintaining accurate directionality across multiple input images. Consequently, diffusion models struggled with tasks requiring precise control over geometric space as specified in the text inputs. Experimental results suggest that even when the text provides sufficient spatial information, diffusion models still struggle to map it onto image space. We believe this is because diffusion models tend to extract object information from the text and often overlook attributes like elevation. A potential improvement for diffusion models could involve training them on a large corpus of text-elevation map data for terrain synthesis.