One Channel to Rule Them All: Rethinking Input Representation for Visual Place Recognition

• URL: http://arxiv.org/abs/2606.00936v1
• Date: Sun, 31 May 2026 00:30:05 GMT
• Title: One Channel to Rule Them All: Rethinking Input Representation for Visual Place Recognition
• Abstract summary: Visual Place Recognition is fundamental to long-term robot localization and SLAM.<n>Current systems overwhelmingly rely on RGB input, implicitly assuming color is necessary for global place recognition.<n>We find that grayscale matches RGB performance generally and outperforms it under severe appearance shifts.
• Score: 14.020214078011515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Place Recognition (VPR) is fundamental to long-term robot localization and SLAM, yet current systems overwhelmingly rely on RGB input, implicitly assuming color is necessary for global place recognition. We challenge this assumption, investigating the role of chromatic information across training regimes, model architectures and standard benchmarks under real-world appearance variation. We find that grayscale matches RGB performance generally and outperforms it under severe appearance shifts where color invariance is insufficiently learned, while color provides meaningful gains only where persistent and discriminative chromatic cues are present. Across selected benchmarks, a fully gray-trained MixVPR model achieves an average 82.4% Recall@1 compared to 81.2% for its RGB counterpart. In some cases, lightweight grayscale variants with 60% fewer parameters can outperform heavier RGB models. Grayscale further offers practical advantages in storage, bandwidth and alignment with resource-constrained systems. We conclude that for global VPR where scenes vary across illumination, weather, season and setting, color contributes minimally, and grayscale alone is sufficient for reliable place recognition.

Related papers

• Retrieval-Augmented Generation-Based Color Restoration for Low-Light Image Enhancement [0.0]
  We recast color restoration as an independent sub-problem, realizing it as a general-purpose post-processing module built on retrieval-augmented generation (RAG)<n>The design has three components: (i) a dual-index FAISS retriever built on intermediate VGG19 features, capturing textural and structural similarity through global mean and variance statistics; (ii) GlobalSPHistAdaIN, which reduces the reference spatial-preserving color histogram to a global color vector and modulates network features via adaptive instance normalization; and (iii) a residual formulation that predicts a color correction over the front-end output.
  arXiv  Detail & Related papers  (2026-08-08T16:12:12Z)
• Color When It Counts: Grayscale-Guided Online Triggering for Always-On Streaming Video Sensing [58.25947399492333]
  We present a new paradigm for efficient streaming video understanding: grayscale-always, color-on-demand.<n>We propose ColorTrigger, an online training-free trigger that activates color capture based on windowed grayscale affinity analysis.<n>On streaming video understanding benchmarks, ColorTrigger achieves 91.6% of full-color baseline performance while using only 8.1% RGB frames.
  arXiv  Detail & Related papers  (2026-03-23T18:32:36Z)
• PEPR: Privileged Event-based Predictive Regularization for Domain Generalization [19.185122873391517]
  We propose a cross-modal framework under the learning using privileged information (LUPI) paradigm for training a robust, single-modality RGB model.<n>We leverage event cameras as a source of privileged information, available only during training.<n>We train the RGB encoder with PEPR to predict event-based latent features, distilling robustness without sacrificing semantic richness.
  arXiv  Detail & Related papers  (2026-02-04T14:10:36Z)
• Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization [0.9064664319018063]
  We present HAQAGen, a unified generative model for resolution-invariant NIR-to-RGB colorization.<n>The proposed model introduces (i) a combined loss term aligning the global color statistics through differentiable histogram matching, perceptual image quality measure, and feature based similarity to preserve texture information.<n>We introduce an adaptive-resolution inference engine that further enables high-resolution translation without sacrificing quality.
  arXiv  Detail & Related papers  (2026-01-03T07:46:59Z)
• VELoRA: A Low-Rank Adaptation Approach for Efficient RGB-Event based Recognition [54.27379947727035]
  This paper proposes a novel PEFT strategy to adapt the pre-trained foundation vision models for the RGB-Event-based classification.<n>The frame difference of the dual modalities is also considered to capture the motion cues via the frame difference backbone network.<n>The source code and pre-trained models will be released on urlhttps://github.com/Event-AHU/VELoRA.
  arXiv  Detail & Related papers  (2024-12-28T07:38:23Z)
• UniRGB-IR: A Unified Framework for Visible-Infrared Semantic Tasks via Adapter Tuning [34.727262809777095]
  We propose UniRGB-IR, a scalable and efficient framework for RGB-IR semantic tasks.<n>Our framework comprises three key components: a vision transformer (ViT) foundation model, a Multi-modal Feature Pool (SFI) module, and a Supplementary Feature (SFI) module.<n> Experimental results on various RGB-IR semantic tasks demonstrate that our method can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-04-26T12:21:57Z)
• Rethinking RGB Color Representation for Image Restoration Models [55.81013540537963]
  We augment the representation to hold structural information of local neighborhoods at each pixel. Substituting the underlying representation space for the per-pixel losses facilitates the training of image restoration models. Our space consistently improves overall metrics by reconstructing both color and local structures.
  arXiv  Detail & Related papers  (2024-02-05T06:38:39Z)
• Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification [9.624510941236837]
  Modality-Aware Multiple Granularity Learning (MMGL) is a self-supervised pre-training alternative to ImageNet pre-training. MMGL learns better representations (+6.47% Rank-1) with faster training speed (converge in few hours) and solider data efficiency (5% data size) than ImageNet pre-training. Results suggest it generalizes well to various existing models, losses and has promising transferability across datasets.
  arXiv  Detail & Related papers  (2021-12-12T04:40:33Z)
• CoRe: Color Regression for Multicolor Fashion Garments [80.57724826629176]
  In this paper, we handle color detection as a regression problem to predict the exact RGB values. We include a second regression stage for refinement in our newly proposed architecture. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment.
  arXiv  Detail & Related papers  (2020-10-06T16:12:30Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• Synergistic saliency and depth prediction for RGB-D saliency detection [76.27406945671379]
  Existing RGB-D saliency datasets are small, which may lead to overfitting and limited generalization for diverse scenarios. We propose a semi-supervised system for RGB-D saliency detection that can be trained on smaller RGB-D saliency datasets without saliency ground truth.
  arXiv  Detail & Related papers  (2020-07-03T14:24:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.