In this report, we present the 1st place solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction. The challenge aims to evaluate approaches for novel view synthesis and surface reconstruction using only a few posed images of each object. We utilize Pixel-NeRF as the basic model, and apply depth supervision as well as coarse-to-fine positional encoding. The experiments demonstrate the effectiveness of our approach in improving sparse-view reconstruction quality. We ranked first in the final test with a PSNR of 25.44614.

翻译：本报告介绍了我们在ICCV 2023 OmniObject3D挑战赛稀疏视角重建任务中提出的冠军方案。该挑战赛旨在评估仅利用每个物体的少量带位姿图像进行新视角合成与表面重建的方法。我们采用Pixel-NeRF作为基础模型，并引入深度监督与粗细粒度位置编码机制。实验结果表明，本方法能有效提升稀疏视角重建质量。最终测试中以25.44614的PSNR值排名第一。