Classification-based depth completion methods have achieved remarkable performance. However, the result is still coarse due to the limitation of using unified depth categories to represent depth distribution. In this work, we propose CustomDepth which can customize exclusive depth categories for each image point to boost performance. To this end, CustomDepth introduces a depth subdivision module that allocates adaptive depth categories for each point based on its properties, instead of refining a set of unified categories for all points. With these adaptive depth categories, CustomDepth utilizes a binary classifier to determine whether a point is located in front of or behind each depth category. The classification results are then accumulated using a rendering approach to calculate the final depth result. To reduce computational burden, CustomDepth also incorporates an image subdivision module that selectively processes a subset of error-prone points. Extensive experiments demonstrate that CustomDepth is a lightweight and flexible framework that achieves competitive performance compared to existing classification-based methods.

中文翻译：

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CustomDepth：自定义逐点深度类别以完成深度

基于分类的深度补全方法取得了显着的性能。然而，由于使用统一深度类别来表示深度分布的限制，结果仍然很粗糙。在这项工作中，我们提出了 CustomDepth，它可以为每个图像点定制专有的深度类别以提高性能。为此，CustomDepth引入了深度细分模块，该模块根据每个点的属性为每个点分配自适应深度类别，而不是为所有点细化一组统一的类别。通过这些自适应深度类别，CustomDepth 利用二元分类器来确定点是位于每个深度类别的前面还是后面。然后使用渲染方法累积分类结果以计算最终的深度结果。为了减少计算负担，CustomDepth 还集成了一个图像细分模块，可以选择性地处理容易出错的点的子集。大量实验表明，CustomDepth 是一个轻量级且灵活的框架，与现有的基于分类的方法相比，它具有具有竞争力的性能。