Year: 2025
Type: conference-paper
Abstract: Colorization is a well-explored problem in the domains of image and video processing. However, extending colorization to 3D scenes presents significant challenges. Re-cent Neural Radiance Field (NeRF) and Gaussian-Splatting (3DGS) methods enable high-quality novel-view synthe-sis for multi-view images. However, the question arises: How can we colorize these 3D representations? This work presents a method for synthesizing colorized novel views from input grayscale multi-view images. Using image or video colorization methods to colorize novel views from these 3D representations naively will yield output with se-vere inconsistencies. We introduce a novel method to use powerful image colorization models for colorizing 3D representations. We propose a distillation-based method that transfers color from these networks trained on natural images to the target 3D representation. Notably, this strat-egy does not add any additional weights or computational overhead to the original representation during inference. Extensive experiments demonstrate that our method produces high-quality colorized views for indoor and outdoor scenes, showcasing significant cross-view consistency advantages over baseline approaches. Our method is agnos-tic to the underlying 3D representation and easily gener-alizable to NeRF and 3DGS methods. Further, we vali-date the efficacy of our approach in several diverse applications: 1.) Infra-Red (IR) multi-view images and 2.) Legacy grayscale multi-view image sequences. Project Webpage: https://val.cds.iisc.ac.in/chroma-distill.github.io/
Authors: Ankit Dhiman, R Srinath, Srinjay Sarkar, Lokesh R Boregowda, R Venkatesh Babu
Institutions: Indian Institute of Science Bangalore, Samsung (India)
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409261779