User-in-the-Loop View Sampling with Error Peaking Visualization
Augmented reality (AR) provides ways to visualize missing view samples for novel view synthesis. Existing approaches present 3D annotations for new view samples and task users with taking images by aligning the AR display. This data collection task is known to be mentally demanding and limits capture areas to pre-defined small areas due to the ideal but restrictive underlying sampling theory. To free users from 3D annotations and limited scene exploration, we propose using locally reconstructed light fields and visualizing errors to be removed by inserting new views. Our results show that the error-peaking visualization is less invasive, reduces disappointment in final results, and is satisfactory with fewer view samples in our mobile view synthesis system. We also show that our approach can contribute to recent radiance field reconstruction for larger scenes, such as 3D Gaussian splatting.
Code (0)
등록된 구현이 없습니다.
Tasks
Novel View SynthesisSimilar Papers 제목 키워드 기반
Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review
Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research ha…
Negative Sampling in Recommendation: A Survey and Future Directions
Recommender systems aim to capture users' personalized preferences from the cast amount of user behaviors, making them pivotal in the era of information explosion. However, the presence of the dynamic preference, the "in…
Recommendation SystemsSurveyPhoneme-Level Mispronunciation Screening in Polish-Speaking Children with an Explainable Assistant
Early identification of speech sound errors in children is often limited by access to specialists, motivating lightweight screening tools that can operate outside the clinic. We present a screening pipeline for Polish-sp…
StepAudio 3 Realtime Technical Report
Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loo…
EmbodiedHead: Real-Time Listening and Speaking Avatar for Conversational Agents
We present EmbodiedHead, a speech-driven talking-head framework that equips LLMs with real-time visual avatars for conversation. A practical embodied avatar must achieve real-time generation, unified listening-speaking b…