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Takayoshi Yamashita

15 accepted papers

2026

P2GS: Physical Prior-guided Gaussian Splatting for Photometrically Consistent Urban Reconstruction

CVPR 2026

3D Gaussian Splatting (3DGS) has recently emerged as a powerful explicit representation enabling fast, high-fidelity rendering, making it a promising foundation for closed-loop simulators and perception models in autonomous driving. However, conventional 3DGS implicitly assumes consistent exposure a

Cited by 0SourceScholar
2025

OD-RASE: Ontology-Driven Risk Assessment and Safety Enhancement for Autonomous Driving

ICCV 2025poster

Although autonomous driving systems demonstrate high perception performance, they still face limitations when handling rare situations or complex road structures. Since existing road infrastructures are designed for human drivers, safety improvements are typically introduced only after accidents occ…

Cited by 0SourcePDFScholar
2024

Active Domain Adaptation with False Negative Prediction for Object Detection

CVPR 2024highlight

Domain adaptation adapts models to various scenes with different appearances. In this field active domain adaptation is crucial in effectively sampling a limited number of data in the target domain. We propose an active domain adaptation method for object detection focusing on quantifying the undete…

Cited by 4SourcePDFScholar
2024

Deep Single Image Camera Calibration by Heatmap Regression to Recover Fisheye Images Under Manhattan World Assumption

CVPR 2024poster

A Manhattan world lying along cuboid buildings is useful for camera angle estimation. However accurate and robust angle estimation from fisheye images in the Manhattan world has remained an open challenge because general scene images tend to lack constraints such as lines arcs and vanishing points.…

Cited by 3SourcePDFScholar
2023

This Looks Like It Rather Than That: ProtoKNN For Similarity-Based Classifiers

ICLR 2023poster

Among research on the interpretability of deep learning models, the 'this looks like that' framework with ProtoPNet has attracted significant attention. By combining the strong power of deep learning models with the interpretability of case-based inference, ProtoPNet can achieve high accuracy while…

Cited by 13SourcePDFScholar
2022

Deep Ensemble Learning by Diverse Knowledge Distillation for Fine-Grained Object Classification

ECCV 2022poster

"Ensemble of networks with bidirectional knowledge distillation does not significantly improve on the performance of ensemble of networks without bidirectional knowledge distillation. We think that this is because there is a relationship between the knowledge in knowledge distillation and the indivi…

Cited by 11SourcePDFScholar
2022

Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion

ECCV 2022poster

"Although recent learning-based calibration methods can predict extrinsic and intrinsic camera parameters from a single image, the accuracy of these methods is degraded in fisheye images. This degradation is caused by mismatching between the actual projection and expected projection. To address this…

Cited by 9SourcePDFScholar
2021

Iterative Coarse-to-Fine 6D-Pose Estimation Using Back-propagation

IROS 2021poster

We propose a 6D pose estimation method for an object from a single RGB image for a robotic grasping task. Many approaches estimate pose parameters from images taken from other viewpoints and use deep learning to achieve high accuracy. However, most of these methods are not robust to changes in objec…

Cited by 3SourceScholar
2020

Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network

RA-L 2020

Domestic service robots (DSRs) are a promising solution to the shortage of home care workers. However, one of the main limitations of DSRs is their inability to interact naturally through language. Recently, data-driven approaches have been shown to be effective for tackling this limitation; however

Cited by 12SourceScholar
2020

MT-DSSD: Deconvolutional Single Shot Detector Using Multi Task Learning for Object Detection, Segmentation, and Grasping Detection

ICRA 2020poster

This paper presents the multi-task Deconvolutional Single Shot Detector (MT-DSSD), which runs three tasks-object detection, semantic object segmentation, and grasping detection for a suction cup-in a single network based on the DSSD. Simultaneous execution of object detection and segmentation by mul…

Cited by 40SourceScholar
2019

Attention Branch Network: Learning of Attention Mechanism for Visual Explanation

CVPR 2019oral

Visual explanation enables humans to understand the decision making of deep convolutional neural network (CNN), but it is insufficient to contribute to improving CNN performance. In this paper, we focus on the attention map for visual explanation, which represents a high response value as the attent…

Cited by 640PDFcodeScholar
2019

Detecting layered structures of partially occluded objects for bin picking

IROS 2019poster

When robots engage in bin picking of multiple objects, a failure in grasping partially occluded objects may occur because other objects may overlap the desired ones. Therefore, the layered structure of objects needs to be detected, and the picking order needs to be established. In this paper, we pro…

Cited by 21SourceScholar
2019

Fast and Precise Detection of Object Grasping Positions with Eigenvalue Templates

ICRA 2019poster

Fast Graspability Evaluation (FGE) has been proposed as a method for detecting grasping positions on objects and is now being used for industrial robots. FGE uses convolution of hand templates with regions on the target object to estimate the optimum grasping posture. However, the hand opening width…

Cited by 5SourceScholar
2015

Fast 3D edge detection by using decision tree from depth image

IROS 2015poster

T3D edge detection from a depth image is an important technique of 3D object recognition in preprocessing. There are three types of 3D edges in a depth image called jump, convex roof, and concave roof edges. Conventional 3D edge detection based on ring operators has been proposed. The conventional r…

Cited by 4SourceScholar
2015

Multiple-Hypothesis Affine Region Estimation With Anisotropic LoG Filters

ICCV 2015poster

We propose a method for estimating multiple-hypothesis affine regions from a keypoint by using an anisotropic Laplacian-of-Gaussian (LoG) filter. Although conventional affine region detectors, such as Hessian/Harris-Affine, iterate to find an affine region that fits a given image patch, such iterati…

Cited by 10PDFScholar