Abstract
We propose SparseDC, a model for Depth Completion of Sparse and non-uniform depth inputs. Unlike previous methods focusing on completing fixed distributions on benchmark datasets (e.g., NYU with 500 points, KITTI with 64 lines), SparseDC is specifically designed to handle depth maps with poor quality in real usage. The key contributions of SparseDC are two-fold. First, we design a simple strategy, called SFFM, to improve the robustness under sparse input by explicitly filling the unstable depth features with stable image features. Second, we propose a two-branch feature embedder to predict both the precise local geometry of regions with available depth values and accurate structures in regions with no depth. The key of the embedder is an uncertainty-based fusion module called UFFM to balance the local and long-term information extracted by CNNs and ViTs. Extensive indoor and outdoor experiments demonstrate the robustness of our framework when facing sparse and non-uniform input depths. The pre-trained model and code are available at this https URL.
Abstract (translated)
我们提出了SparseDC，一种用于处理稀疏和非均匀深度输入的模型。与之前专注于在基准数据集上完成固定分布的方法（例如，NYU具有500个点，KITTI具有64行）不同，SparseDC专门设计来处理实际使用中深度图的质量较差的情况。SparseDC的关键贡献是双重的。首先，我们设计了一个简单的策略，称为SFFM，通过明确填充不稳定深度特征来提高在稀疏输入下的鲁棒性。其次，我们提出了一个两分支的特征嵌入器来预测既有可用深度值的区域的精确局部几何，又有没有深度值的区域的准确结构。嵌入器的关键是一个基于不确定性的融合模块，称为UFFM，平衡了CNN和ViTs提取的局部和长期信息。在室内和室外实验中，我们的框架在面对稀疏和非均匀输入深度时表现出极高的鲁棒性。预训练模型和代码可在此处访问：https://www.spark-computing.org/zh/project/sparse-dc。
URL
https://arxiv.org/abs/2312.00097
