746 citations · 860 across the 29 of their papers we have counts for
29 papers
HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection
Jiyong Boo, Byeongin Joung, Hyemin Yang +1
Monocular 3D lane detection plays a critical role in autonomous driving, yet recovering reliable 3D geometry from a single image remains challenging due to inherent depth ambiguity…
Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields
Taewoo Kim, Yujeong Chae, Hyun-Kurl Jang +1
Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brigh…
Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events
Hoonhee Cho, Hyeonseong Kim, Yujeong Chae +1
Recognizing objects from sparse and noisy events becomes extremely difficult when paired images and category labels do not exist. In this paper, we study label-free event-based obj…
Cognitive TransFuser: Semantics-guided Transformer-based Sensor Fusion for Improved Waypoint Prediction
Hwan-Soo Choi, Jongoh Jeong, Young Hoo Cho +2
Sensor fusion approaches for intelligent self-driving agents remain key to driving scene understanding given visual global contexts acquired from input sensors. Specifically, for t…
Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction
Daehee Park, Hobin Ryu, Yunseo Yang +3
Understanding the interaction between multiple agents is crucial for realistic vehicle trajectory prediction. Existing methods have attempted to infer the interaction from the obse…
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation
Inkyu Shin, Dahun Kim, Qihang Yu +6
Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level scene understanding by segmenting all pixels and associating objects in a video. Current solutions can b…
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Taeyeop Lee, Jonathan Tremblay, Valts Blukis +6
Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requir…
MATE: Masked Autoencoders are Online 3D Test-Time Learners
M. Jehanzeb Mirza, Inkyu Shin, Wei Lin +8
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring…
One-Shot Neural Fields for 3D Object Understanding
Valts Blukis, Taeyeop Lee, Jonathan Tremblay +5
We present a unified and compact scene representation for robotics, where each object in the scene is depicted by a latent code capturing geometry and appearance. This representati…
Learning Monocular Depth Estimation via Selective Distillation of Stereo Knowledge
Kyeongseob Song, Kuk-Jin Yoon
Monocular depth estimation has been extensively explored based on deep learning, yet its accuracy and generalization ability still lag far behind the stereo-based methods. To tackl…
Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networks
Pranjay Shyam, Sandeep Singh Sengar, Kuk-Jin Yoon +1
The limited dynamic range of commercial compact camera sensors results in an inaccurate representation of scenes with varying illumination conditions, adversely affecting image qua…
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
Inkyu Shin, Yi-Hsuan Tsai, Bingbing Zhuang +5
Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and expl…
DGSS : Domain Generalized Semantic Segmentation using Iterative Style Mining and Latent Representation Alignment
Pranjay Shyam, Antyanta Bangunharcana, Kuk-Jin Yoon +1
Semantic segmentation algorithms require access to well-annotated datasets captured under diverse illumination conditions to ensure consistent performance. However, poor visibility…
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation
Youngho Yoon, Inchul Chung, Lin Wang +1
The 360°imaging has recently gained great attention; however, its angular resolution is relatively lower than that of a narrow field-of-view (FOV) perspective image as it is captur…
BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning
Changgyoon Oh, Wonjune Cho, Daehee Park +3
Providing omnidirectional depth along with RGB information is important for numerous applications, eg, VR/AR. However, as omnidirectional RGB-D data is not always available, synthe…
Pixel-wise Deep Image Stitching
Hyeokjun Kweon, Hyeonseong Kim, Yoonsu Kang +3
Image stitching aims at stitching the images taken from different viewpoints into an image with a wider field of view. Existing methods warp the target image to the reference image…
Exploring Pixel-level Self-supervision for Weakly Supervised Semantic Segmentation
Sung-Hoon Yoon, Hyeokjun Kweon, Jaeseok Jeong +3
Existing studies in weakly supervised semantic segmentation (WSSS) have utilized class activation maps (CAMs) to localize the class objects. However, since a classification loss is…
Event-guided Deblurring of Unknown Exposure Time Videos
Taewoo Kim, Jeongmin Lee, Lin Wang +1
Motion deblurring is a highly ill-posed problem due to the loss of motion information in the blur degradation process. Since event cameras can capture apparent motion with a high t…
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation
Lin Wang, Yujeong Chae, Sung-Hoon Yoon +2
Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over conventional cameras. A…
Deep Learning for HDR Imaging: State-of-the-Art and Future Trends
Lin Wang, Kuk-Jin Yoon
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision.…
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Taeyeop Lee, Byeong-Uk Lee, Inkyu Shin +4
Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real w…
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation
Lin Wang, Yujeong Chae, Kuk-Jin Yoon
Event cameras are novel sensors that perceive the per-pixel intensity changes and output asynchronous event streams with high dynamic range and less motion blur. It has been shown…
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks
Pranjay Shyam, Sandeep Singh Sengar, Kuk-Jin Yoon +1
Region modification-based data augmentation techniques have shown to improve performance for high level vision tasks (object detection, semantic segmentation, image classification,…
Towards Domain Invariant Single Image Dehazing
Pranjay Shyam, Kuk-Jin Yoon, Kyung-Soo Kim
Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing al…