1,452 research outputs found
Learning monocular 3D reconstruction of articulated categories from motion
Monocular 3D reconstruction of articulated object categories is challenging
due to the lack of training data and the inherent ill-posedness of the problem.
In this work we use video self-supervision, forcing the consistency of
consecutive 3D reconstructions by a motion-based cycle loss. This largely
improves both optimization-based and learning-based 3D mesh reconstruction. We
further introduce an interpretable model of 3D template deformations that
controls a 3D surface through the displacement of a small number of local,
learnable handles. We formulate this operation as a structured layer relying on
mesh-laplacian regularization and show that it can be trained in an end-to-end
manner. We finally introduce a per-sample numerical optimisation approach that
jointly optimises over mesh displacements and cameras within a video, boosting
accuracy both for training and also as test time post-processing. While relying
exclusively on a small set of videos collected per category for supervision, we
obtain state-of-the-art reconstructions with diverse shapes, viewpoints and
textures for multiple articulated object categories.Comment: For project website see
https://fkokkinos.github.io/video_3d_reconstruction
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
We focus on the challenging task of real-time semantic segmentation in this
paper. It finds many practical applications and yet is with fundamental
difficulty of reducing a large portion of computation for pixel-wise label
inference. We propose an image cascade network (ICNet) that incorporates
multi-resolution branches under proper label guidance to address this
challenge. We provide in-depth analysis of our framework and introduce the
cascade feature fusion unit to quickly achieve high-quality segmentation. Our
system yields real-time inference on a single GPU card with decent quality
results evaluated on challenging datasets like Cityscapes, CamVid and
COCO-Stuff.Comment: ECCV 201
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