13 accepted papers
Although recent work has explored generative modeling of 3D or 4D driving scenes, most approaches operate on dense voxel-based representations, which are computationally expensive and struggle to maintain temporal or structural consistency. These methods often produce blurred or merged entities (i.e
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
While recent advancements in camera-based 3D object detection demonstrate remarkable performance, they require thousands or even millions of human-annotated frames. This requirement significantly inhibits their deployment in various locations and sensor configurations. To address this gap, we propos…
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…
"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…
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…
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
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…
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…
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…
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…
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…
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…