Training for X-Ray Vision: Amodal Segmentation, Amodal Content
  Completion, and View-Invariant Object Representation from Multi-Camera Video
Authors: name: Alexander Moore, name: Amar Saini, name: Kylie Cancilla, name: Doug Poland, name: Carmen Carrano

Amodal segmentation and amodal content completion require using object priors
to estimate occluded masks and features of objects in complex scenes. Until
now, no data has provided an additional dimension for object context: the
possibility of multiple cameras sharing a view of a scene. We introduce
MOVi-MC-AC: Multiple Object Video with Multi-Cameras and Amodal Content, the
largest amodal segmentation and first amodal content dataset to date. Cluttered
scenes of generic household objects are simulated in multi-camera video.
MOVi-MC-AC contributes to the growing literature of object detection, tracking,
and segmentation by including two new contributions to the deep learning for
computer vision world. Multiple Camera (MC) settings where objects can be
identified and tracked between various unique camera perspectives are rare in
both synthetic and real-world video. We introduce a new complexity to synthetic
video by providing consistent object ids for detections and segmentations
between both frames and multiple cameras each with unique features and motion
patterns on a single scene. Amodal Content (AC) is a reconstructive task in
which models predict the appearance of target objects through occlusions. In
the amodal segmentation literature, some datasets have been released with
amodal detection, tracking, and segmentation labels. While other methods rely
on slow cut-and-paste schemes to generate amodal content pseudo-labels, they do
not account for natural occlusions present in the modal masks. MOVi-MC-AC
provides labels for ~5.8 million object instances, setting a new maximum in the
amodal dataset literature, along with being the first to provide ground-truth
amodal content. The full dataset is available at
https://huggingface.co/datasets/Amar-S/MOVi-MC-AC ,