Selected Publications
Semantic Photometric Bundle Adjustment on Natural Sequences
We provide the first approach of its kind (to our knowledge) for semantic object-centric PBA on natural sequences – which gives the global 6DoF camera poses of each frame and the dense 3D shape, with PBA-like accuracy but denser depth maps.
arXiv preprint
Learning Depth from Monocular Videos using Direct Methods
For learning single image depth predictor from monocular sequences, we show that the depth CNN predictor can be learned without a pose CNN predictor, by incorporatin of a differentiable implementation of DVO, along with a novel depth normalization strategy.
arXiv preprint
Rethinking Reprojection: Closing the Loop for Pose-aware Shape Reconstruction from a Single Image
In this paper we define the new task of pose-aware shape reconstruction from a single image, and we design architectures of pose-aware shape reconstruction using weak constraint from reprojecting the predicted shape back on to the image with the predicted pose.
In ICCV 2017 [Spotlight]
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
We introduce learned shape prior in the form of deep shape generators into Photometric Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by the shape prior within the optimization-based inference framework, demonstrating impressive results.
In WACV 2018
