论文标题
多视图表演者以完成形状
Multiple View Performers for Shape Completion
论文作者
论文摘要
我们提出了多视图表演者（MVP） - 从一系列时间顺序的视图中完成3D形状完成的新体系结构。 MVP通过使用称为表演者的线性注意变压器来完成此任务。我们的模型允许当前对场景的观察到以前的观察，以更准确地填充。过去观察的历史通过紧凑的关联内存来压缩，该记忆近似于现代连续的霍普菲尔德内存，但至关重要的是与历史长度无关。我们将模型与几个基线进行比较，以便随着时间的推移完成形状完成，这证明了MVP提供的概括增长。据我们所知，MVP是第一个多个视图体素重建方法，它不需要对多个深度视图进行注册，也是第一个基于因果变压器的基于因果变压器完成3D形状完成的模型。
We propose the Multiple View Performer (MVP) - a new architecture for 3D shape completion from a series of temporally sequential views. MVP accomplishes this task by using linear-attention Transformers called Performers. Our model allows the current observation of the scene to attend to the previous ones for more accurate infilling. The history of past observations is compressed via the compact associative memory approximating modern continuous Hopfield memory, but crucially of size independent from the history length. We compare our model with several baselines for shape completion over time, demonstrating the generalization gains that MVP provides. To the best of our knowledge, MVP is the first multiple view voxel reconstruction method that does not require registration of multiple depth views and the first causal Transformer based model for 3D shape completion.
