180 research outputs found
Structural textile pattern recognition and processing based on hypergraphs
The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following a certain taxonomy, but search functionality at the level of thread structure is still missing. To facilitate the clustering and search, we introduce an approach for recognising similar weaving patterns based on their structures for textile archives. We first represent textile structures using hypergraphs and extract multisets of k-neighbourhoods describing weaving patterns from these graphs. Then, the resulting multisets are clustered using various distance measures and various clustering algorithms (K-Means for simplicity and hierarchical agglomerative algorithms for precision). We evaluate the different variants of our approach experimentally, showing that this can be implemented efficiently (meaning it has linear complexity), and demonstrate its quality to query and cluster datasets containing large textile samples. As, to the best of our knowledge, this is the first practical approach for explicitly modelling complex and irregular weaving patterns usable for retrieval, we aim at establishing a solid baseline
Open-Vocabulary Affordance Detection in 3D Point Clouds
Affordance detection is a challenging problem with a wide variety of robotic
applications. Traditional affordance detection methods are limited to a
predefined set of affordance labels, hence potentially restricting the
adaptability of intelligent robots in complex and dynamic environments. In this
paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method,
which is capable of detecting an unbounded number of affordances in 3D point
clouds. By simultaneously learning the affordance text and the point feature,
OpenAD successfully exploits the semantic relationships between affordances.
Therefore, our proposed method enables zero-shot detection and can be able to
detect previously unseen affordances without a single annotation example.
Intensive experimental results show that OpenAD works effectively on a wide
range of affordance detection setups and outperforms other baselines by a large
margin. Additionally, we demonstrate the practicality of the proposed OpenAD in
real-world robotic applications with a fast inference speed (~100ms). Our
project is available at https://openad2023.github.io.Comment: Accepted to The 2023 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2023
Language-driven Scene Synthesis using Multi-conditional Diffusion Model
Scene synthesis is a challenging problem with several industrial
applications. Recently, substantial efforts have been directed to synthesize
the scene using human motions, room layouts, or spatial graphs as the input.
However, few studies have addressed this problem from multiple modalities,
especially combining text prompts. In this paper, we propose a language-driven
scene synthesis task, which is a new task that integrates text prompts, human
motion, and existing objects for scene synthesis. Unlike other single-condition
synthesis tasks, our problem involves multiple conditions and requires a
strategy for processing and encoding them into a unified space. To address the
challenge, we present a multi-conditional diffusion model, which differs from
the implicit unification approach of other diffusion literature by explicitly
predicting the guiding points for the original data distribution. We
demonstrate that our approach is theoretically supportive. The intensive
experiment results illustrate that our method outperforms state-of-the-art
benchmarks and enables natural scene editing applications. The source code and
dataset can be accessed at https://lang-scene-synth.github.io/.Comment: Accepted to NeurIPS 202
Direct measurement of mechanical vibrations of the 4-rod RFQ at the HLI
In this paper, we present a new haptic interface, called active skin , which is configured with a tactile sensor and a tactile stimulator in single haptic cell, and multiple haptic cells are embedded in a dielectric elastomer. The active skin generates a wide variety of haptic feel in response to the touch by synchronizing the sensor and the stimulator. In this paper, the design of the haptic cell is derived via iterative analysis and design procedures. A fabrication method dedicated to the proposed device is investigated and a controller to drive multiple haptic cells is developed. In addition, several experiments are performed to evaluate the performance of the active skin
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