Point clouds are widely used in various fields, including augmented reality (AR) and robotics, where relighting and texture editing are crucial for realistic visualization. Achieving these tasks requires accurately separating albedo from shade. However, performing this separation on point clouds presents two key challenges: (1) the non-grid structure of point clouds makes conventional image-based decomposition models ineffective, and (2) point-cloud models designed for other tasks do not explicitly consider global-light direction, resulting in inaccurate shade. In this paper, we introduce Intrinsic Point-Cloud Decomposition (IPCD), which extends image decomposition to the direct decomposition of colored point clouds into albedo and shade. To overcome challenge (1), we propose IPCD-Net that extends image-based model with point-wise feature aggregation for non-grid data processing. For challenge (2), we introduce Projection-based Luminance Distribution (PLD) with a hierarchical feature refinement, capturing global-light ques via multi-view projection. For comprehensive evaluation, we create a synthetic outdoor-scene dataset. Experimental results demonstrate that IPCD-Net reduces cast shadows in albedo and enhances color accuracy in shade. Furthermore, we showcase its applications in texture editing, relighting, and point-cloud registration under varying illumination. Finally, we verify the real-world applicability of IPCD-Net.

Intrinsic image decomposition (IID) is the task of separating an image into albedo and shade. In real-world scenes, it is difficult to quantitatively assess IID quality due to the unavailability of ground truth. The existing method provides the relative reflection intensities based on human-judged annotations. However, these annotations have challenges in subjectivity, relative evaluation, and hue non-assessment. To address these, we propose a concept of quantitative evaluation with a calculated albedo from a hyperspectral imaging and light detection and ranging (LiDAR) intensity. Additionally, we introduce an optional albedo densification approach based on spectral similarity. This paper conducted a concept verification in a laboratory environment, and suggested the feasibility of an objective, absolute, and hue-aware assessment.1

Unsupervised intrinsic image decomposition (IID) is the task of separating a natural image into albedo and shade without ground truth during training. Although a recent model employing light detection and ranging (LiDAR) intensity demonstrated impressive performance, the necessity of LiDAR intensity during inference restricts its practicality. To expand the usage scenario while maintaining the IID quality achieved by using both an image and its corresponding LiDAR intensity, we propose a novel approach that utilizes an image without LiDAR intensity during inference while utilizing both an image and LiDAR intensity during training. Specifically, our proposed model processes an image and LiDAR intensity individually using distinct encoder paths during training, but utilizes only an imageencoder path during inference. Additionally, we introduce an albedo-alignment loss aligning the gray-scale albedo from an image to that from its corresponding LiDAR intensity. LiDAR intensity is not affected by illumination effects including cast shadows, thus albedo-alignment loss transfers the illumination-invariant property of LiDAR intensity to the image-encoder path. Furthermore, we also propose image-LiDAR conversion (ILC) paths that mutually translates the style of an image and LiDAR intensity. IID models translate an image into albedo and shade styles while keeping the image contents, thus it is important to separate the image into contents and style. Trained with pairs of an image and its corresponding LiDAR intensity which share contents but differ in style, the mutual translation in ILC paths improve the accuracy of the separation. Consequently, our model achieves comparable IID quality to the existing model with LiDAR intensity, while utilizing only an image without LiDAR intensity during inference.

While recent Vision-Language (VL) models excel at open-vocabulary tasks, it is unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image Retrieval (TIR) or Generation (TIG) are recently introduced tasks that represent this challenge, where the VL model has to learn a concept from few images and respectively discriminate or generate images of the target concept in arbitrary contexts. We identify the ability to learn new meanings and their compositionality with known ones as two key properties of a personalized system. We show that the available benchmarks offer a limited validation of personalized textual concept learning from images with respect to the above properties and introduce ConCon-Chi as a benchmark for both personalized TIR and TIG, designed to fill this gap. We modelled the new-meaning concepts by crafting chimeric objects and formulating a large, varied set of contexts where we photographed each object. To promote the compositionality assessment of the learned concepts with known contexts, we combined different contexts with the same concept, and vice-versa. We carry out a thorough evaluation of state-of-the-art methods on the resulting dataset. Our study suggests that future work on personalized TIR and TIG methods should focus on the above key properties, and we propose principles and a dataset for their performance assessment. Dataset: https://doi.org/10.48557/QJ1166 and code: https://github.com/hsp-iit/concon-chi_benchmark.

Recent advances in deep learning have improved 3D point cloud registration but increased graphics processing unit (GPU) memory usage, often requiring preliminary sampling that reduces accuracy. We propose an overlapping region sampling method to reduce memory usage while maintaining accuracy. Our approach estimates the overlapping region and intensively samples from it, using a k-nearest-neighbor (kNN) based point compression mechanism with multi layer perceptron (MLP) and transformer architectures. Evaluations on 3DMatch and 3DLoMatch datasets show our method outperforms other sampling methods in registration recall, especially at lower GPU memory levels. For 3DMatch, we achieve 94% recall with 33% reduced memory usage, with greater advantages in 3DLoMatch. Our method enables efficient large-scale point cloud registration in resource-constrained environments, maintaining high accuracy while significantly reducing memory requirements.

In this paper, we address the problem of mapping identifiers in non-RDF data to URIs. Virtual knowledge graphs (VKGs), where non-RDF data, such as relational databases, CSV files, etc., are published as RDF data, allowing users to access them using a standard query language (SPARQL), has been gaining much attention to integrating heterogeneous data. There have been several VKG systems, but there has been a problem of assigning an appropriate URI to an entity included in a record, and existing systems only support simple methods to generate a URI by adding a URI prefix to the ID value in a record. However, in practice, more complex mappings are needed to meet the demands of real applications. To address this problem, we proposed to extend the relation-to-RDF mapping rules where users are allowed to specify how entities in relations are mapped to URIs in terms of a user-defined URI function. More precisely, we integrate this method into our relation-to-RDF mapping framework. We conduct a set of experiments to assess the feasibility of the proposed method.

Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods.