In this work, we propose DeepIM, a new refinement technique based on a deep neural network for iterative 6D pose matching. Given an initial 6D pose estimation of an object in a test image, DeepIM predicts a relative SE(3) trans- formation that matches a rendered view of the object against the observed image.

We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. By the introduction of position-senstive inside/outside score maps, the underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest.

We propose a novel 6-DoF pose estimation approach: Coordinates-based Disentangled Pose Network (CDPN), which disentangles the pose to predict rotation and translation separately to achieve highly accurate and robust pose estimation.

We propose a novel and specially designed method for piecewise dense monocular depth estimation in dynamic scenes. We utilize spatial relations between neighboring superpixels to solve the inherent relative scale ambiguity (RSA) problem and smooth the depth map.

We present a novel image acquisition framework capable of reconstructing high bit-depth images using an array of low bit-depth scalar quantizers. Through the collaborative design of the sampling end and the reconstruction end, we achieve high-quality image acquisition and reconstruction.