13 citations · 45 across the 20 of their papers we have counts for
20 papers
Text2Scene: Text-driven Indoor Scene Stylization with Part-aware Details
Inwoo Hwang, Hyeonwoo Kim, Young Min Kim
We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our p…
LDL: Line Distance Functions for Panoramic Localization
Junho Kim, Changwoon Choi, Hojun Jang +1
We introduce LDL, a fast and robust algorithm that localizes a panorama to a 3D map using line segments. LDL focuses on the sparse structural information of lines in the scene, whi…
Calibrating Panoramic Depth Estimation for Practical Localization and Mapping
Junho Kim, Eun Sun Lee, Young Min Kim
The absolute depth values of surrounding environments provide crucial cues for various assistive technologies, such as localization, navigation, and 3D structure estimation. We pro…
PMP: Learning to Physically Interact with Environments using Part-wise Motion Priors
Jinseok Bae, Jungdam Won, Donggeun Lim +2
We present a method to animate a character incorporating multiple part-wise motion priors (PMP). While previous works allow creating realistic articulated motions from reference da…
Balanced Spherical Grid for Egocentric View Synthesis
Changwoon Choi, Sang Min Kim, Young Min Kim
We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficient…
Privacy-Preserving Visual Localization with Event Cameras
Junho Kim, Young Min Kim, Ramzi Zahreddine +4
We consider the problem of client-server localization, where edge device users communicate visual data with the service provider for locating oneself against a pre-built 3D map. Th…
MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents
Eun Sun Lee, Junho Kim, SangWon Park +1
We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides import…
IBL-NeRF: Image-Based Lighting Formulation of Neural Radiance Fields
Changwoon Choi, Juhyeon Kim, Young Min Kim
We propose IBL-NeRF, which decomposes the neural radiance fields (NeRF) of large-scale indoor scenes into intrinsic components. Recent approaches further decompose the baked radian…
CPO: Change Robust Panorama to Point Cloud Localization
Junho Kim, Hojun Jang, Changwoon Choi +1
We present CPO, a fast and robust algorithm that localizes a 2D panorama with respect to a 3D point cloud of a scene possibly containing changes. To robustly handle scene changes,…
Ev-NeRF: Event Based Neural Radiance Field
Inwoo Hwang, Junho Kim, Young Min Kim
We present Ev-NeRF, a Neural Radiance Field derived from event data. While event cameras can measure subtle brightness changes in high frame rates, the measurements in low lighting…
Probabilistic Implicit Scene Completion
Dongsu Zhang, Changwoon Choi, Inbum Park +1
We propose a probabilistic shape completion method extended to the continuous geometry of large-scale 3D scenes. Real-world scans of 3D scenes suffer from a considerable amount of…
Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition
Junho Kim, Inwoo Hwang, Young Min Kim
We introduce Ev-TTA, a simple, effective test-time adaptation algorithm for event-based object recognition. While event cameras are proposed to provide measurements of scenes with…
Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video
Jinseok Bae, Hojun Jang, Cheol-Hui Min +2
We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with…
N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras
Junho Kim, Jaehyeok Bae, Gangin Park +2
We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object recognition with event cameras. The dataset is collected using programmable hardware in whic…
SGoLAM: Simultaneous Goal Localization and Mapping for Multi-Object Goal Navigation
Junho Kim, Eun Sun Lee, Mingi Lee +2
We present SGoLAM, short for simultaneous goal localization and mapping, which is a simple and efficient algorithm for Multi-Object Goal navigation. Given an agent equipped with an…
PICCOLO: Point Cloud-Centric Omnidirectional Localization
Junho Kim, Changwoon Choi, Hojun Jang +1
We present PICCOLO, a simple and efficient algorithm for omnidirectional localization. Given a colored point cloud and a 360 panorama image of a scene, our objective is to recover…
GATSBI: Generative Agent-centric Spatio-temporal Object Interaction
Cheol-Hui Min, Jinseok Bae, Junho Lee +1
We present GATSBI, a generative model that can transform a sequence of raw observations into a structured latent representation that fully captures the spatio-temporal context of t…
Learning to Generate 3D Shapes with Generative Cellular Automata
Dongsu Zhang, Changwoon Choi, Jeonghwan Kim +1
We present a probabilistic 3D generative model, named Generative Cellular Automata, which is able to produce diverse and high quality shapes. We formulate the shape generation proc…
Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
Dongsu Zhang, Junha Chun, Sang Kyun Cha +1
We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning. The raw 3D reconstruction of an ind…
RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion
Muhammad Sarmad, Hyunjoo Jenny Lee, Young Min Kim
We present RL-GAN-Net, where a reinforcement learning (RL) agent provides fast and robust control of a generative adversarial network (GAN). Our framework is applied to point cloud…