10,777 research outputs found
Dimensions of Motion: Monocular Prediction through Flow Subspaces
We introduce a way to learn to estimate a scene representation from a single
image by predicting a low-dimensional subspace of optical flow for each
training example, which encompasses the variety of possible camera and object
movement. Supervision is provided by a novel loss which measures the distance
between this predicted flow subspace and an observed optical flow. This
provides a new approach to learning scene representation tasks, such as
monocular depth prediction or instance segmentation, in an unsupervised fashion
using in-the-wild input videos without requiring camera poses, intrinsics, or
an explicit multi-view stereo step. We evaluate our method in multiple
settings, including an indoor depth prediction task where it achieves
comparable performance to recent methods trained with more supervision.Comment: Project page at https://dimensions-of-motion.github.io
Robust Motion Segmentation from Pairwise Matches
In this paper we address a classification problem that has not been
considered before, namely motion segmentation given pairwise matches only. Our
contribution to this unexplored task is a novel formulation of motion
segmentation as a two-step process. First, motion segmentation is performed on
image pairs independently. Secondly, we combine independent pairwise
segmentation results in a robust way into the final globally consistent
segmentation. Our approach is inspired by the success of averaging methods. We
demonstrate in simulated as well as in real experiments that our method is very
effective in reducing the errors in the pairwise motion segmentation and can
cope with large number of mismatches
- …