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Found 16 papers, 10 papers with code
All-day Depth Completion via Thermal-LiDAR Fusion
However, due to the limitations of RGB sensors, existing methods often struggle to achieve reliable performance in harsh environments, such as heavy rain and low-light conditions.
Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges
Achieving robust and accurate spatial perception under adverse weather and lighting conditions is crucial for the high-level autonomy of self-driving vehicles and robots.
Federated Domain Generalization with Data-free On-server Gradient Matching
In this paper, we introduce a novel approach, dubbed Federated Learning via On-server Matching Gradient (FedOMG), which can \emph{efficiently leverage domain information from distributed domains}.
CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection
Recent LiDAR-based 3D Object Detection (3DOD) methods show promising results, but they often do not generalize well to target domains outside the source (or training) data distribution.
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.
Lightweight Alpha Matting Network Using Distillation-Based Channel Pruning
Therefore, there has been a demand for a lightweight alpha matting model due to the limited computational resources of commercial portable devices.
Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in Semantic Segmentation
In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation.
MC-Calib: A generic and robust calibration toolbox for multi-camera systems
In this paper, we present MC-Calib, a novel and robust toolbox dedicated to the calibration of complex synchronized multi-camera systems using an arbitrary number of fiducial marker-based patterns.
SALT : Sharing Attention between Linear layer and Transformer for tabular dataset
The proposed SALT consists of two blocks: Transformers and linear layers blocks that take advantage of shared attention matrices.
Non-Local Spatial Propagation Network for Depth Completion
In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion.
Ranked #1 on
Depth Completion
on NYU-Depth V2
Propose-and-Attend Single Shot Detector
We present a simple yet effective prediction module for a one-stage detector.
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.
Efficient adaptive non-maximal suppression algorithms for homogeneous spatial keypoint distribution
Keypoint detection usually results in a large number of keypoints which are mostly clustered, redundant, and noisy.
Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting.
A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation
In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation.
Ranked #2 on
Defocus Estimation
on CUHK - Blur Detection Dataset
Accurate Depth Map Estimation From a Lenslet Light Field Camera
This paper introduces an algorithm that accurately estimates depth maps using a lenslet light field camera.
