IntelliCap: Intelligent Guidance for Consistent View Sampling

2025-08-18

AI Summary

In real-world scenarios, uneven and insufficient human-captured viewpoints degrade novel-view synthesis quality. To address this, we propose a multi-scale contextualized visual guidance method. Our approach integrates semantic segmentation with vision-language models to identify key objects and prioritize them, constructs spherical proxy regions to encode viewpoint-dependent appearance details, and provides real-time hierarchical visual instructions to guide users in acquiring dense, spatially uniform image collections. Compared to conventional sampling strategies, our method significantly improves viewpoint coverage density and spatial uniformity in practical settings, thereby enhancing the visual fidelity of novel-view synthesis—particularly for emerging rendering techniques such as 3D Gaussian splatting. The core contribution lies in the organic unification of semantic understanding, geometric proxy modeling, and human-in-the-loop guidance, establishing an intelligent scene scanning paradigm tailored for high-fidelity 3D reconstruction.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Novel view synthesis from images, for example, with 3D Gaussian splatting, has made great progress. Rendering fidelity and speed are now ready even for demanding virtual reality applications. However, the problem of assisting humans in collecting the input images for these rendering algorithms has received much less attention. High-quality view synthesis requires uniform and dense view sampling. Unfortunately, these requirements are not easily addressed by human camera operators, who are in a hurry, impatient, or lack understanding of the scene structure and the photographic process. Existing approaches to guide humans during image acquisition concentrate on single objects or neglect view-dependent material characteristics. We propose a novel situated visualization technique for scanning at multiple scales. During the scanning of a scene, our method identifies important objects that need extended image coverage to properly represent view-dependent appearance. To this end, we leverage semantic segmentation and category identification, ranked by a vision-language model. Spherical proxies are generated around highly ranked objects to guide the user during scanning. Our results show superior performance in real scenes compared to conventional view sampling strategies.

Problem

• Assisting humans in collecting input images for rendering algorithms
• Ensuring uniform and dense view sampling for high-quality synthesis
• Guiding users to capture view-dependent appearance of important objects

Innovation

• Uses semantic segmentation for object identification
• Employs vision-language model for ranking
• Generates spherical proxies for user guidance