3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis. Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding. A key challenge is the inconsistency of 2D instance labels across views, leading to poor 3D predictions. Existing methods use a two-stage approach in which some rely on contrastive learning with hyperparameter-sensitive clustering, while others preprocess labels for consistency. We propose a unified framework that merges these steps, reducing training time and improving performance by introducing a learnable feature embedding for segmentation in Gaussian primitives. This embedding is then efficiently decoded into instance labels through a novel "Embedding-to-Label" process, effectively integrating the optimization. While this unified framework offers substantial benefits, we observed artifacts at the object boundaries. To address the object boundary issues, we propose hard-mining samples along these boundaries. However, directly applying hard mining to the feature embeddings proved unstable. Therefore, we apply a linear layer to the rasterized feature embeddings before calculating the triplet loss, which stabilizes training and significantly improves performance. Our method outperforms baselines qualitatively and quantitatively on the ScanNet, Replica3D, and Messy-Rooms datasets.

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/

Neural Radiance Field (NeRF) approaches learn the underlying 3D representation of a scene and generate photo-realistic novel views with high fidelity. However, most proposed settings concentrate on modelling a single object or a single level of a scene. However, in the real world, we may capture a scene at multiple levels, resulting in a layered capture. For example, tourists usually capture a monument’s exterior structure before capturing the inner structure. Modelling such scenes in 3D with seamless switching between levels can drastically improve immersive experiences. However, most existing techniques struggle in modelling such scenes. We propose Strata-NeRF, a single neural radiance field that implicitly captures a scene with multiple levels. Strata-NeRF achieves this by conditioning the NeRFs on Vector Quantized (VQ) latent representations which allow sudden changes in scene structure. We evaluate the effectiveness of our approach in multi-layered synthetic dataset comprising diverse scenes and then further validate its generalization on the real-world RealEstate10K dataset. We find that Strata-NeRF effectively captures stratified scenes, minimizes artifacts, and synthesizes high-fidelity views compared to existing approaches. https://ankitatiisc.github.io/Strata-NeRF/