arXiv subjects

Charlie Li-Ting Tsai

Publications and source records attributed to Charlie Li-Ting Tsai.

3 recordsLinked to original sources

arXiv2026

DF3DV-1K: A Large-Scale Dataset and Benchmark for Distractor-Free Novel View Synthesis

Advances in radiance fields have enabled photorealistic novel view synthesis. In several domains, large-scale real-world datasets have been developed to support comprehensive benchmarking and to facilitate progress beyond scene-specific reconstruction. However, for distractor-free radiance fields, a large-scale dataset with clean and cluttered images per scene remains lacking, limiting the development. To address this gap, we introduce DF3DV-1K, a large-scale real-world dataset comprising 1,048 scenes, each providing clean and cluttered image sets for benchmarking. In total, the dataset contains 89,924 images captured using consumer cameras to mimic casual capture, spanning 128 distractor types and 161 scene themes across indoor and outdoor environments. A curated subset of 41 scenes, DF3DV-41, is systematically designed to evaluate the robustness of distractor-free radiance field methods under challenging scenarios. Using DF3DV-1K, we benchmark nine recent distractor-free radiance field methods and 3D Gaussian Splatting, identifying the most robust methods and the most challenging scenarios. Beyond benchmarking, we demonstrate an application of DF3DV-1K by fine-tuning a diffusion-based 2D enhancer to improve radiance field methods, achieving average improvements of 0.96 dB PSNR and 0.057 LPIPS on the held-out set (e.g., DF3DV-41) and the On-the-go dataset. We hope DF3DV-1K facilitates the development of distractor-free vision and promotes progress beyond scene-specific approaches. The dataset and leaderboard are available at https://johnnylu305.github.io/df3dv1k_web/.

cs.CV↗

arXiv2026

SASLO: A Scene-Aware Spatial Layout Optimization System for AR-SSVEP

Steady-state visual evoked potential (SSVEP) is widely used in brain-computer interfaces (BCIs) due to its reliability. With the integration of augmented reality (AR), AR-SSVEP enables more intuitive interaction by embedding visual stimuli into real-world environments. However, unlike conventional computer screen-based SSVEP (CS-SSVEP) systems with stable visual conditions, AR-SSVEP performance is influenced by real-world scene factors, such as luminance and color, which degrade stimulus perception and weaken SSVEP elicitation. Nevertheless, existing studies primarily focus on offline analyses of SSVEP-related factors in indoor settings, while online adaptive optimization for outdoor AR-SSVEP remains limited. Therefore, a scenario-aware spatial layout optimization (SASLO) system for AR-SSVEP is proposed, which jointly considers scene luminance and inter-stimulus distance (ISD) for adaptive stimulus layout optimization. Scene luminance is estimated using an RGB-CIE based method, and the extracted context is incorporated into a linear contextual bandit (LCB) model to recommend optimized spatial layouts. Two pilot single-factor experiments are conducted to characterize the effects of luminance and ISD on SSVEP performance and to construct reliable rewards for model training. An outdoor online experiment with ten subjects further validates the proposed joint optimization method, achieving an average accuracy of 0.89 and an information transfer rate of 35.74 bits/min with a 3 s input window, and consistently outperforming two baseline methods. Overall, the proposed SASLO system is shown to improve the robustness of AR-SSVEP in real-world outdoor environments.

cs.HC↗

arXiv2025

EMD-Fuzzy: An Empirical Mode Decomposition Based Fuzzy Model for Cross-Stimulus Transfer Learning of SSVEP

The Brain-Computer Interface (BCI) enables direct brain-to-device communication, with the Steady-State Visual Evoked Potential (SSVEP) paradigm favored for its stability and high accuracy across various fields. In SSVEP BCI systems, supervised learning models significantly enhance performance over unsupervised models, achieving higher accuracy in less time. However, prolonged data collection can cause user fatigue and even trigger photosensitive epilepsy, creating a negative user experience. Thus, reducing calibration time is crucial. To address this, Cross-Stimulus transfer learning (CSTL) can shorten calibration by utilizing only partial frequencies. Traditional CSTL methods, affected by time-domain impulse response variations, are suitable only for adjacent frequency transfers, limiting their general applicability. We introduce an Empirical Mode Decomposition (EMD) Based Fuzzy Model (EMD-Fuzzy), which employs EMD to extract crucial frequency information and achieves stimulus transfer in the frequency domain through Fast Fourier Transform (FFT) to mitigate time-domain differences. Combined with a Fuzzy Decoder that uses fuzzy logic for representation learning, our approach delivers promising preliminary results in offline tests and state-of-the-art performance. With only 4 frequencies, our method achieved an accuracy of 82.75% (16.30%) and an information transfer rate (ITR) of 186.56 (52.09) bits/min on the 40-target Benchmark dataset. In online tests, our method demonstrates robust efficacy, achieving an averaged accuracy of 86.30% (6.18%) across 7 subjects. This performance underscores the effectiveness of integrating EMD and fuzzy logic into EEG decoding for CSTL and highlights our method's potential in real-time applications where consistent and reliable decoding is crucial.

cs.HC↗