74 citations · 186 across the 16 of their papers we have counts for
20 papers
Neural 3D Clothes Retargeting from a Single Image
Jae Shin Yoon, Kihwan Kim, Jan Kautz +1
In this paper, we present a method of clothes retargeting; generating the potential poses and deformations of a given 3D clothing template model to fit onto a person in a single RG…
Online Adaptation for Consistent Mesh Reconstruction in the Wild
Xueting Li, Sifei Liu, Shalini De Mello +4
This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild. Without requiring annotations of 3D mesh, 2D…
DeepGMR: Learning Latent Gaussian Mixture Models for Registration
Wentao Yuan, Ben Eckart, Kihwan Kim +3
Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics. For the last few decades, existing registration algorithms have struggled in situati…
Bi3D: Stereo Depth Estimation via Binary Classifications
Abhishek Badki, Alejandro Troccoli, Kihwan Kim +3
Stereo-based depth estimation is a cornerstone of computer vision, with state-of-the-art methods delivering accurate results in real time. For several applications such as autonomo…
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera
Jae Shin Yoon, Kihwan Kim, Orazio Gallo +2
This paper presents a new method to synthesize an image from arbitrary views and times given a collection of images of a dynamic scene. A key challenge for the novel view synthesis…
Two-shot Spatially-varying BRDF and Shape Estimation
Mark Boss, Varun Jampani, Kihwan Kim +2
Capturing the shape and spatially-varying appearance (SVBRDF) of an object from images is a challenging task that has applications in both computer vision and graphics. Traditional…
Self-supervised Single-view 3D Reconstruction via Semantic Consistency
Xueting Li, Sifei Liu, Kihwan Kim +4
We learn a self-supervised, single-view 3D reconstruction model that predicts the 3D mesh shape, texture and camera pose of a target object with a collection of 2D images and silho…
Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
Xueting Li, Sifei Liu, Kihwan Kim +3
Affordance modeling plays an important role in visual understanding. In this paper, we aim to predict affordances of 3D indoor scenes, specifically what human poses are afforded by…
NRMVS: Non-Rigid Multi-View Stereo
Matthias Innmann, Kihwan Kim, Jinwei Gu +4
Scene reconstruction from unorganized RGB images is an important task in many computer vision applications. Multi-view Stereo (MVS) is a common solution in photogrammetry applicati…
Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera
Chao Liu, Jinwei Gu, Kihwan Kim +2
Depth sensing is crucial for 3D reconstruction and scene understanding. Active depth sensors provide dense metric measurements, but often suffer from limitations such as restricted…
PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image
Chen Liu, Kihwan Kim, Jinwei Gu +2
This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces from a single RGB image. PlaneRCNN employs a variant of Mask R-CN…
Neural Inverse Rendering of an Indoor Scene from a Single Image
Soumyadip Sengupta, Jinwei Gu, Kihwan Kim +3
Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single…
EOE: Expected Overlap Estimation over Unstructured Point Cloud Data
Ben Eckart, Kihwan Kim, Jan Kautz
We present an iterative overlap estimation technique to augment existing point cloud registration algorithms that can achieve high performance in difficult real-world situations wh…
Fast and Accurate Point Cloud Registration using Trees of Gaussian Mixtures
Ben Eckart, Kihwan Kim, Jan Kautz
Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, SLAM, object/scene recognition, and augmented re…
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Anurag Ranjan, Varun Jampani, Lukas Balles +4
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation…
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli +3
Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is common…
Geometry-Aware Learning of Maps for Camera Localization
Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim +2
Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose…
Multiframe Scene Flow with Piecewise Rigid Motion
Vladislav Golyanik, Kihwan Kim, Robert Maier +3
We introduce a novel multiframe scene flow approach that jointly optimizes the consistency of the patch appearances and their local rigid motions from RGB-D image sequences. In con…
Intrinsic3D: High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting
Robert Maier, Kihwan Kim, Daniel Cremers +2
We introduce a novel method to obtain high-quality 3D reconstructions from consumer RGB-D sensors. Our core idea is to simultaneously optimize for geometry encoded in a signed dist…
A Lightweight Approach for On-the-Fly Reflectance Estimation
Kihwan Kim, Jinwei Gu, Stephen Tyree +3
Estimating surface reflectance (BRDF) is one key component for complete 3D scene capture, with wide applications in virtual reality, augmented reality, and human computer interacti…