论文标题
稀疏不可知的深度完成
Sparsity Agnostic Depth Completion
论文作者
论文摘要
我们为深度点的稀疏性提出了一种新颖的深度完成方法不可知，这在许多实际应用中很可能会有所不同。最先进的方法仅在处理特定的密度和输入点的分布时，即在训练过程中观察到的特定密度和分布时，才能得出准确的结果，从而在实际用例中缩小其部署。相反，我们的解决方案对于不均匀的分布和训练期间从未见过的极低密度非常强大。标准室内和室外基准测试的实验结果突出了我们框架的鲁棒性，当用密度和分布等于训练的测试时，在其他情况下以训练的密度和分布相等，在其他情况下，实现了与最先进的方法相当的准确性。我们验证的模型和其他材料可在我们的项目页面中找到。
We present a novel depth completion approach agnostic to the sparsity of depth points, that is very likely to vary in many practical applications. State-of-the-art approaches yield accurate results only when processing a specific density and distribution of input points, i.e. the one observed during training, narrowing their deployment in real use cases. On the contrary, our solution is robust to uneven distributions and extremely low densities never witnessed during training. Experimental results on standard indoor and outdoor benchmarks highlight the robustness of our framework, achieving accuracy comparable to state-of-the-art methods when tested with density and distribution equal to the training one while being much more accurate in the other cases. Our pretrained models and further material are available in our project page.
