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Supplementary materials for the paper on REBIS.

We present the Place-NeRFs, a scalable approach to large-scale 3D scene reconstruction that subdivides scenes into non-overlapping regions that can be handled by off-the-shelf NeRF models, striking a balance between reconstruction quality and efficient use of computational resources. By leveraging rough single-view depth estimation and visibility graphs, Place-NeRFs effectively groups spatially correlated photospheres, enabling independent volumetric reconstructions. This approach significantly reduces processing time and enhances scalability during NeRF models' training.

In image recognition, knowledge distillation is a valuable approach to train a compact model with high accuracy by exploiting outputs of a highly accurate large model as correct labels. In knowledge distillation, studies have shown the usefulness of data with high entropy output generated by image mix data augmentation techniques. Other strategies such as curriculum learning have also been proposed to improve model generalization by the control of the difficulty of training data over the learning process.

Supplemental material

ABCD

A Neural Radiance Field (NeRF) is capable of representing scenes by capturing view-dependent properties from a specific set of images through neural network training. The lack of a significant initial image set can lead to a subsequent photographing session and training to improve the final view synthesis. For this purpose, we introduce a new variant of NeRF training analysis, termed the Neural Error Field (NEF). NEF visualizes and identifies view-dependent errors to reduce the number of ray samples used in the follow-up training.

Supplementary Materials for Iterative Self-Improvement of Vision Language Models for Image Scoring and Self-Explanation

The supplementary material contains information on the generated strokes for each sketch,
the filenames of the sketch animation GIFs, the prompts used for the sketches,
and illustrations of the separation process for individual objects.

Additionally, there is a GIFs.zip file inside Supplements.zip.
One can check the generated GIF animations with that.

Supplementary Materials for ICIP 2025

Recent advancements in text-driven 3D content generation highlight several challenges. Surveys show that users often provide simple text inputs while expecting high-quality results. Generating optimal 3D content from minimal prompts is difficult due to the strong dependency of text-to-3D models on input quality. Moreover, the generation process exhibits high variability, often requiring many attempts to meet user expectations, reducing efficiency. To address this, we propose GPT-4V for self-optimization, enhancing generation efficiency and enabling satisfactory results in a single attempt.