Search Results for author:
Found 9 papers, 2 papers with code
Learning to Control Camera Exposure via Reinforcement Learning
Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of computer vision applications.
Stable Surface Regularization for Fast Few-Shot NeRF
This paper proposes an algorithm for synthesizing novel views under few-shot setup.
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Unlike previous unsupervised domain adaptation methods for category-level object pose estimation, our approach processes the test data in a sequential, online manner, and it does not require access to the source domain at runtime.
Single View Scene Scale Estimation Using Scale Field
In this paper, we propose a single image scale estimation method based on a novel scale field representation.
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion
Based on the analysis, we propose an effective thermal image mapping method that significantly increases image information, such as overall structure, contrast, and details, while preserving temporal consistency.
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Inspired by recent multi-modal UDA techniques, the proposed method exploits a teacher-student self-supervised learning scheme to train a pose estimation network without using target domain pose labels.
Ranked #5 on
6D Pose Estimation using RGBD
on REAL275
Category-Level Metric Scale Object Shape and Pose Estimation
Our framework has two branches: the Metric Scale Object Shape branch (MSOS) and the Normalized Object Coordinate Space branch (NOCS).
Depth Completion using Plane-Residual Representation
The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data.
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Deep reinforcement learning (DRL) algorithms and evolution strategies (ES) have been applied to various tasks, showing excellent performances.
