Daily indoor scenes often involve constant changes due to human activities. To recognize scene changes, existing change captioning methods focus on describing changes from two images of a scene. However, to accurately perceive and appropriately evaluate physical changes and then identify the geometry of changed objects, recognizing and localizing changes in 3D space is crucial. Therefore, we propose a task to explicitly localize changes in 3D bounding boxes from two point clouds and describe detailed scene changes, including change types, object attributes, and spatial locations. Moreover, we create a simulated dataset with various scenes, allowing generating data without labor costs. We further propose a framework that allows different 3D object detectors to be incorporated in the change detection process, after which captions are generated based on the correlations of different change regions. The proposed framework achieves promising results in both change detection and captioning. Furthermore, we also evaluated on data collected from real scenes. The experiments show that pretraining on the proposed dataset increases the change detection accuracy by +12.8% (mAP0.25) when applied to real-world data. We believe that our proposed dataset and discussion could provide both a new benchmark and in-sights for future studies in scene change understanding.

In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision Transformers (ViTs). For example, ViT-Base pre-trained on ImageNet-21k shows 81.8% top-1 accuracy when fine-tuned on ImageNet-1k and FDSL shows 82.7% top-1 accuracy when pre-trained under the same conditions (number of images, hyperparameters, and number of epochs). Images generated by formulas avoid the privacy/copyright issues, labeling cost and errors, and biases that real images suffer from, and thus have tremendous potential for pre-training general models. To understand the performance of the synthetic images, we tested two hypotheses, namely (i) object contours are what matter in FDSL datasets and (ii) increased number of parameters to create labels affects performance improvement in FDSL pre-training. To test the former hypothesis, we constructed a dataset that consisted of simple object contour combinations. We found that this dataset can match the performance of fractals. For the latter hypothesis, we found that increasing the difficulty of the pre-training task generally leads to better fine-tuning accuracy.

The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a large-scale 3D point clouds dataset is difficult. In order to rem-edy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-driven supervised learning inspired by fractal geometry encountered in natural 3D structures. Our re-search is based on the hypothesis that we could learn rep-resentations from more real-world 3D patterns than con-ventional 3D datasets by learning fractal geometry. We show how the PC-FractalDB facilitates solving several re-cent dataset-related problems in 3D scene understanding, such as 3D model collection and labor-intensive annotation. The experimental section shows how we achieved the performance rate of up to 61.9% and 59.0% for the Scan-NetV2 and SUN RGB-D datasets, respectively, over the current highest scores obtained with the PointContrast, con-trastive scene contexts (CSC), and RandomRooms. More-over, the PC-FractalDB pre-trained model is especially ef-fective in training with limited data. For example, in 10% of training data on ScanNetV2, the PC-FractalDB pre-trained VoteNet performs at 38.3%, which is +14.8% higher accu-racy than CSC. Of particular note, we found that the pro-posed method achieves the highest results for 3D object de-tection pre-training in limited point cloud data.11Dataset release: https://ryosuke-yamada.github.io/PointCloud-FractalDataBase/

Abstract Is it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven Supervised Learning (FDSL). We automatically generate image patterns and their category labels by assigning fractals, which are based on a natural law. Theoretically, the use of automatically generated images instead of natural images in the pre-training phase allows us to generate an infinitely large dataset of labeled images. The proposed framework is similar yet different from Self-Supervised Learning because the FDSL framework enables the creation of image patterns based on any mathematical formulas in addition to self-generated labels. Further, unlike pre-training with a synthetic image dataset, a dataset under the framework of FDSL is not required to define object categories, surface texture, lighting conditions, and camera viewpoint. In the experimental section, we find a better dataset configuration through an exploratory study, e.g., increase of #category/#instance, patch rendering, image coloring, and training epoch. Although models pre-trained with the proposed Fractal DataBase (FractalDB), a database without natural images, do not necessarily outperform models pre-trained with human annotated datasets in all settings, we are able to partially surpass the accuracy of ImageNet/Places pre-trained models. The FractalDB pre-trained CNN also outperforms other pre-trained models on auto-generated datasets based on FDSL such as Bezier curves and Perlin noise. This is reasonable since natural objects and scenes existing around us are constructed according to fractal geometry. Image representation with the proposed FractalDB captures a unique feature in the visualization of convolutional layers and attentions.

The paper proposes a method for automatic multi-view dataset construction based on formula-driven supervised learning (FDSL). Although data collection and human annotation of 3D objects are labor-intensive, we automatically generate their training data and labels in the proposed multi-view dataset. To create a large-scale multi-view dataset, we employ fractal geometry, which is considered the background information of many objects in the real world. We project in a circle from the rendered 3D fractal models to construct the Multi-view Fractal DataBase (MV-FractalDB), which is then used to make a pre-trained CNN model. According to the experimental results, the MV-FractalDB pre-trained model surpasses the accuracies with self-supervised methods (e.g., SimCLR and MoCo) and is close to supervised methods (e.g., ImageNet) in terms of performance rates on multi-view image datasets. We demonstrate the potential of FDSL for multi-view image recognition.

A control method of a Matrix Converter to convert from four-wired three-phase voltages to three-wired single-phase voltages (single-phase MC) which replaces transformer in consumer side on micro-grid has been found [1]. In the single-phase MC, voltage drop and phase shift in its load voltage occur because the line current flows due to the input and output filters. So we proposed the load voltage control method for the single-phase MC [2] [3] to compensate them to supply symmetrical two-phase AC load voltages for consumer side. However, when the output load changed to no-load, the output side of the single-phase MC became only LC filter left and resonance was caused but it could not be suppressed by the conventional load voltage control. So we should propose a new method to suppress it. In this paper a damping control method for the single-phase MC is proposed, and the effectiveness of the proposed method is verified by simulation results.