41 citations · 146 across the 21 of their papers we have counts for
23 papers
SceneBench: A Hierarchical Benchmark for Vision-Language Understanding of 3D Scenes
Anubhav Khanal, Prabigya Acharya, Roshni Poudel +5
Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current benchmarks. First, 3D datasets oft…
Seeing Through the Weights: Privacy Leakage in Scene Coordinate Regression
Oleksii Nasypanyi, Jaemin Cho, Utku Ozbulak +2
Scene Coordinate Regression (SCR) methods are increasingly adopted for visual localization. In these approaches, the scene is implicitly encoded within a neural network that regres…
Pixel-Accurate Epipolar Guided Matching
Oleksii Nasypanyi, Francois Rameau
Keypoint matching can be slow and unreliable in challenging conditions such as repetitive textures or wide-baseline views. In such cases, known geometric relations (e.g., the funda…
360 in the Wild: Dataset for Depth Prediction and View Synthesis
Kibaek Park, Francois Rameau, Jaesik Park +1
The large abundance of perspective camera datasets facilitated the emergence of novel learning-based strategies for various tasks, such as camera localization, single image depth e…
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
Chaoning Zhang, Joseph Cho, Fachrina Dewi Puspitasari +11
The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentat…
Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited Learning
Juyoung Yun, Sol Choi, Francois Rameau +2
With the increasing complexity of machine learning models, managing computational resources like memory and processing power has become a critical concern. Mixed precision techniqu…
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
Chenghao Li, Chaoning Zhang, Joseph Cho +6
Generative AI has made significant progress in recent years, with text-guided content generation being the most practical as it facilitates interaction between human instructions a…
InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning
Juyeb Shin, Hyeonjun Jeong, Francois Rameau +1
For scalable autonomous driving, a robust map-based localization system, independent of GPS, is fundamental. To achieve such map-based localization, online high-definition (HD) map…
Labeling Where Adapting Fails: Cross-Domain Semantic Segmentation with Point Supervision via Active Selection
Fei Pan, Francois Rameau, Junsik Kim +1
Training models dedicated to semantic segmentation requires a large amount of pixel-wise annotated data. Due to their costly nature, these annotations might not be available for th…
Keypoints Tracking via Transformer Networks
Oleksii Nasypanyi, Francois Rameau
In this thesis, we propose a pioneering work on sparse keypoints tracking across images using transformer networks. While deep learning-based keypoints matching have been widely in…
Deep Point Cloud Reconstruction
Jaesung Choe, Byeongin Joung, Francois Rameau +2
Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and compl…
PointMixer: MLP-Mixer for Point Cloud Understanding
Jaesung Choe, Chunghyun Park, Francois Rameau +2
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer. Despite its simplicity compared to transformer, the concept of channel-mixing MLPs and t…
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Seokju Lee, Francois Rameau, Fei Pan +1
Estimating the motion of the camera together with the 3D structure of the scene from a monocular vision system is a complex task that often relies on the so-called scene rigidity a…
VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction
Jaesung Choe, Sunghoon Im, Francois Rameau +2
To reconstruct a 3D scene from a set of calibrated views, traditional multi-view stereo techniques rely on two distinct stages: local depth maps computation and global depth maps f…
Restoration of Video Frames from a Single Blurred Image with Motion Understanding
Dawit Mureja Argaw, Junsik Kim, Francois Rameau +2
We propose a novel framework to generate clean video frames from a single motion-blurred image. While a broad range of literature focuses on recovering a single image from a blurre…
Stereo Object Matching Network
Jaesung Choe, Kyungdon Joo, Francois Rameau +1
This paper presents a stereo object matching method that exploits both 2D contextual information from images as well as 3D object-level information. Unlike existing stereo matching…
Optical Flow Estimation from a Single Motion-blurred Image
Dawit Mureja Argaw, Junsik Kim, Francois Rameau +2
In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in…
Motion-blurred Video Interpolation and Extrapolation
Dawit Mureja Argaw, Junsik Kim, Francois Rameau +1
Abrupt motion of camera or objects in a scene result in a blurry video, and therefore recovering high quality video requires two types of enhancements: visual enhancement and tempo…
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw +5
ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseN…
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Fei Pan, Inkyu Shin, Francois Rameau +2
Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. However, these approaches heavily rely on annotated data which are labor i…
Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment
Ukcheol Shin, Jinsun Park, Gyumin Shim +2
In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture the best-exposed image which can boost the performance of var…
Light-weight place recognition and loop detection using road markings
Oleksandr Bailo, Francois Rameau, In So Kweon
In this paper, we propose an efficient algorithm for robust place recognition and loop detection using camera information only. Our pipeline purely relies on spatial localization a…
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
Jae Shin Yoon, Francois Rameau, Junsik Kim +3
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from…