20 citations · 57 across the 21 of their papers we have counts for
21 papers
DeePoint: Visual Pointing Recognition and Direction Estimation
Shu Nakamura, Yasutomo Kawanishi, Shohei Nobuhara +1
In this paper, we realize automatic visual recognition and direction estimation of pointing. We introduce the first neural pointing understanding method based on two key contributi…
TransPoser: Transformer as an Optimizer for Joint Object Shape and Pose Estimation
Yuta Yoshitake, Mai Nishimura, Shohei Nobuhara +1
We propose a novel method for joint estimation of shape and pose of rigid objects from their sequentially observed RGB-D images. In sharp contrast to past approaches that rely on c…
InCrowdFormer: On-Ground Pedestrian World Model From Egocentric Views
Mai Nishimura, Shohei Nobuhara, Ko Nishino
We introduce an on-ground Pedestrian World Model, a computational model that can predict how pedestrians move around an observer in the crowd on the ground plane, but from just the…
Fooling Polarization-based Vision using Locally Controllable Polarizing Projection
Zhuoxiao Li, Zhihang Zhong, Shohei Nobuhara +2
Polarization is a fundamental property of light that encodes abundant information regarding surface shape, material, illumination and viewing geometry. The computer vision communit…
Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection
Tomoki Ichikawa, Yoshiki Fukao, Shohei Nobuhara +1
Computer vision applications have heavily relied on the linear combination of Lambertian diffuse and microfacet specular reflection models for representing reflected radiance, whic…
ViewBirdiformer: Learning to recover ground-plane crowd trajectories and ego-motion from a single ego-centric view
Mai Nishimura, Shohei Nobuhara, Ko Nishino
We introduce a novel learning-based method for view birdification, the task of recovering ground-plane trajectories of pedestrians of a crowd and their observer in the same crowd j…
BlindSpotNet: Seeing Where We Cannot See
Taichi Fukuda, Kotaro Hasegawa, Shinya Ishizaki +2
We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage p…
nLMVS-Net: Deep Non-Lambertian Multi-View Stereo
Kohei Yamashita, Yuto Enyo, Shohei Nobuhara +1
We introduce a novel multi-view stereo (MVS) method that can simultaneously recover not just per-pixel depth but also surface normals, together with the reflectance of textureless,…
View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements
Mai Nishimura, Shohei Nobuhara, Ko Nishino
We introduce view birdification, the problem of recovering ground-plane movements of people in a crowd from an ego-centric video captured from an observer (e.g., a person or a vehi…
Differential Viewpoints for Ground Terrain Material Recognition
Jia Xue, Hang Zhang, Ko Nishino +1
Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based repres…
Video Region Annotation with Sparse Bounding Boxes
Yuzheng Xu, Yang Wu, Nur Sabrina binti Zuraimi +2
Video analysis has been moving towards more detailed interpretation (e.g. segmentation) with encouraging progresses. These tasks, however, increasingly rely on densely annotated tr…
Invertible Neural BRDF for Object Inverse Rendering
Zhe Chen, Shohei Nobuhara, Ko Nishino
We introduce a novel neural network-based BRDF model and a Bayesian framework for object inverse rendering, i.e., joint estimation of reflectance and natural illumination from a si…
3D-GMNet: Single-View 3D Shape Recovery as A Gaussian Mixture
Kohei Yamashita, Shohei Nobuhara, Ko Nishino
In this paper, we introduce 3D-GMNet, a deep neural network for 3D object shape reconstruction from a single image. As the name suggests, 3D-GMNet recovers 3D shape as a Gaussian m…
Appearance and Shape from Water Reflection
Ryo Kawahara, Meng-Yu Jennifer Kuo, Shohei Nobuhara +1
This paper introduces single-image geometric and appearance reconstruction from water reflection photography, i.e., images capturing direct and water-reflected real-world scenes. W…
Recognizing Material Properties from Images
Gabriel Schwartz, Ko Nishino
Humans rely on properties of the materials that make up objects to guide our interactions with them. Grasping smooth materials, for example, requires care, and softness is an ideal…
Differential Angular Imaging for Material Recognition
Jia Xue, Hang Zhang, Kristin Dana +1
Material recognition for real-world outdoor surfaces has become increasingly important for computer vision to support its operation "in the wild." Computational surface modeling th…
Material Recognition from Local Appearance in Global Context
Gabriel Schwartz, Ko Nishino
Recognition of materials has proven to be a challenging problem due to the wide variation in appearance within and between categories. Global image context, such as where the mater…
Radiometric Scene Decomposition: Scene Reflectance, Illumination, and Geometry from RGB-D Images
Stephen Lombardi, Ko Nishino
Recovering the radiometric properties of a scene (i.e., the reflectance, illumination, and geometry) is a long-sought ability of computer vision that can provide invaluable informa…
Integrating Local Material Recognition with Large-Scale Perceptual Attribute Discovery
Gabriel Schwartz, Ko Nishino
Material attributes have been shown to provide a discriminative intermediate representation for recognizing materials, especially for the challenging task of recognition from local…
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
Hang Zhang, Kristin Dana, Ko Nishino
Images are the standard input for vision algorithms, but one-shot infield reflectance measurements are creating new opportunities for recognition and scene understanding. In this w…
Reflectance Hashing for Material Recognition
Hang Zhang, Kristin Dana, Ko Nishino
We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing…