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Found 13 papers, 7 papers with code
FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments
This paper presents a stereo thermal depth perception dataset for autonomous aerial perception applications.
VPOcc: Exploiting Vanishing Point for Monocular 3D Semantic Occupancy Prediction
First, in the VPZoomer module, we initially utilize VP in feature extraction to achieve information balanced feature extraction across the scene by generating a zoom-in image based on VP.
Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation
In addition, Flow4D further improves performance by using five frames to take advantage of richer temporal information.
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.
Complementary Random Masking for RGB-Thermal Semantic Segmentation
Also, the proposed self-distillation loss encourages the network to extract complementary and meaningful representations from a single modality or complementary masked modalities.
Ranked #2 on
Thermal Image Segmentation
on PST900
Deep Depth Estimation From Thermal Image
Secondly, we conduct an exhaustive validation process of monocular and stereo depth estimation algorithms designed on visible spectrum bands to benchmark their performance in the thermal image domain.
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning
In this paper, we propose a multi-objective camera ISP framework that utilizes Deep Reinforcement Learning (DRL) and camera ISP toolbox that consist of network-based and conventional ISP tools.
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
Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss
Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks.
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.
Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment
In this paper, we propose a noise-aware exposure control algorithm for robust robot vision.
