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Seeing Through Extreme Visual Sparsity: Surface Understanding from a Single Random Visual Patch

Sindhuja Penchala, Sudip Mittal, Noorbakhsh Amiri Golilarz

arXiv:2608.29475v1cs.CV

TL;DR

Surface material recognition remains difficult when visual observations are incomplete, motivating a framework that combines reconstruction and classification under severe sparsity. SSUF adapts four pretrained architectures to this setting and evaluates them on Touch-and-Go images with only 10% visible. The models show task-specific strengths: Swin leads classification, MAE leads reconstruction, and all remain below 5 ms per image.

  • Problem

    Surface material recognition must infer material characteristics and missing visual information when observations are incomplete.

  • Method

    SSUF adapts ConvAE, ViT, Swin Transformer, and MAE into a unified dual-task framework using random continuous patches containing approximately 10% of each image.

  • Results

    Swin achieved the best classification accuracy at 89.21%, MAE achieved the highest reconstruction PSNR at 16.06 dB, and all models processed images in under 5 ms.

  • Takeaways & Limitations

    Pretrained architectures support material recognition under severe visual sparsity, while accurate image reconstruction remains challenging.

Abstract

from arXiv · show

Surface material recognition from incomplete visual observations remains a challenging problem in robotic perception and environmental understanding. This paper discusses Sparse Surface Understanding Framework (SSUF), a unified dual-task learning framework that adapts four pretrained architectures-Convolutional Autoencoder (ConvAE), Vision Transformer (ViT), Swin Transformer, and Masked Autoencoder (MAE) for si-multaneous surface reconstruction and material classification. Experiments were conducted on the Touch-and-Go dataset using a sparse observation protocol in which only 10% of the original image remained visible while the remaining regions were masked. To enable a fair comparison, reconstruction-oriented models were extended with classification heads, whereas classification- oriented models were augmented with reconstruction decoders. The resulting architectures were assessed using reconstruction quality, classification performance, model complexity, and in-ference efficiency metrics. Experimental results revealed distinct strengths across the models. Swin Transformer achieved the best classification performance with an accuracy of 89.21%, an F1-score of 0.8922, and a ROC-AUC of 0.9813. In contrast, MAE produced the highest reconstruction scores among evaluated models, with a PSNR of 16.06 dB and an SSIM of 0.4501, while ViT provided the best overall balance between reconstruction and classification performance. Furthermore, all models achieved real-time inference, requiring less than 5 ms per image. Over-all, the results show that pretrained architectures can support material recognition under severe visual sparsity, while accurate image reconstruction remains challenging.

I. INTRODUCTION

The paper addresses surface understanding when most visual evidence is unavailable by jointly reconstructing missing content and classifying materials from a random continuous patch containing 10% of the image. SSUF benchmarks four pretrained architectures under this shared sparse-observation setting.

  • Motivation: Surface recognition must operate under incomplete observations caused by occlusion, limited sensor coverage, blur, illumination, and environmental interference.The paper frames reliable understanding as requiring both material recognition and inference of missing visual information.
  • Research gap: Existing work often studies reconstruction and classification separately, assumes randomly distributed visible patches, or lacks identical comparisons across convolutional and transformer architectures.The paper identifies a gap in unified evaluation under localized continuous sparse observations.
  • Contribution: SSUF adapts ConvAE, ViT, Swin Transformer, and MAE for simultaneous surface reconstruction and material classification.The framework evaluates both tasks using pretrained architectures under extreme visual sparsity.
  • Contribution: Reconstruction-oriented and classification-oriented models are converted into a common dual-task paradigm through complementary architectural extensions.This creates a shared basis for comparing the four pretrained model families.
  • Protocol: 10% of the original visual information is preserved through a continuous-patch sparse observation protocol.The visible region is randomly selected rather than fixed at a central location.
  • Evaluation: The study benchmarks reconstruction fidelity, recognition accuracy, computational complexity, and inference efficiency on the Touch-and-Go dataset.The benchmark is designed to compare model strengths across multiple evaluation dimensions.

II. RELATED WORK

Prior surface-material research has advanced visual and multimodal recognition, but the cited approaches primarily classify materials from complete visual observations.

  • Surface material recognition: Early deep learning methods learned material-specific texture and appearance patterns from large-scale datasets such as Materials in Context.These methods established visual learning as a basis for surface material recognition.
  • Surface material recognition: Transformer-based and multimodal frameworks improved recognition by using attention mechanisms and vision-touch fusion.SurFormer and SurFormer v2 are cited as examples of these directions.
  • Research boundary: These approaches primarily focus on classification and assume access to complete visual observations.This leaves sparse-observation reconstruction and recognition outside their main setting.

B. Image Reconstruction from Sparse Observations

The framework positions sparse surface understanding within reconstruction research, pretrained vision models, and a standardized dual-task experimental workflow.

  • Image reconstruction: Image reconstruction methods recover missing visual content, but reconstruction and recognition are typically studied as separate tasks.The related work spans Context Encoders, Partial Convolutions, and transformer-based reconstruction methods.
  • Pretrained vision models: ResNet architectures capture local texture, while ViT and Swin model global and hierarchical visual relationships.These pretrained representations motivate comparing convolutional and transformer backbones.
  • Experimental framework: The experimental framework combines sparse observation generation, architecture-specific model design, and dual-task training.All models use the same sparse-input procedure, dataset partitions, and optimization settings.

A. Sparse Observation Generation and Model Adaptation

The method retains a randomly selected continuous region containing 10% of each image, then adapts all four pretrained architectures to reconstruct the image and classify its material jointly.

  • Sparse observation generation: 10% of each image is retained in a continuous square patch, while the remaining 90% is masked.The patch is randomly selected within image boundaries.
  • Sparse observation generation: The same sparse observation protocol is applied to all four models to ensure a fair comparison.Identical masking conditions prevent the input protocol from favoring one architecture.
  • Model adaptation: Table I compares the evaluated architectures and the modifications introduced to support the common framework.Its stated purpose is architectural comparison rather than reporting performance outcomes.
  • Model adaptation: ConvAE and MAE receive MLP classification heads, whereas ViT and Swin receive CNN reconstruction decoders.These complementary extensions enable every model to perform both tasks.

B. SSUF-Convolutional Autoencoder Framework

SSUF-ConvAE uses a pretrained ResNet50 encoder with shared latent features for reconstruction and material classification, while SSUF-ViT uses patch embeddings and self-attention with an added decoder.

  • SSUF-Convolutional Autoencoder Framework: SSUF-ConvAE uses a pretrained ResNet50 encoder whose features feed a CNN reconstruction decoder and an MLP classification head.The convolutional encoder is described as preserving locality useful for texture and appearance.
  • SSUF-Vision Transformer Framework: SSUF-ViT represents sparse images as patch embeddings and applies transformer encoder layers to capture global contextual relationships through self-attention.A CNN-based decoder supports simultaneous reconstruction and material classification.

D. SSUF-Swin Transformer Framework

The SSUF-Swin framework uses shifted-window self-attention and hierarchical representations for joint reconstruction and classification, while the broader framework adds task-specific modules to pretrained backbones.

  • SSUF-Swin Transformer Framework: SSUF-Swin uses shifted-window self-attention to learn hierarchical multi-scale representations while maintaining computational efficiency.Its shared features support both missing-surface recovery and material recognition.
  • Architecture Adaptation: SSUF-MAE adds an MLP classification head to its latent representation so masked-image reconstruction and material prediction occur simultaneously.The architecture is adapted for dual-task learning.
  • Architecture Adaptation: Task-specific modules are added only as needed to place the four pretrained backbones in a common dual-task setting.This design accommodates different native architectures within SSUF.
  • Sparse Observation Protocol: The framework retains a random continuous patch covering 10% of the image and masks the remaining pixels before processing.This sparse-input protocol is used for the shared evaluation setting.
  • Dual-Task Training: The total objective combines reconstruction and classification losses, with both task weights set to 1.0 and class weights used to mitigate imbalance.The training strategy targets structural reconstruction and semantic material recognition from sparse observations.

IV. EXPERIMENTAL RESULTS AND DISCUSSIONS

The experimental results section evaluates four adapted SSUF architectures across reconstruction quality, material classification, model complexity, and inference efficiency.

  • Experimental Evaluation: Four SSUF architectures are analyzed using reconstruction quality, material classification performance, model complexity, and inference efficiency.The evaluated models are SSUF-ConvAE, SSUF-ViT, SSUF-Swin, and SSUF-MAE.
  • Experimental Evaluation: The framework compares reconstruction and recognition performance within the same sparse-observation experimental setting.This supports a common analysis of the two tasks across architectures.

A. Dataset Description

The study uses the Touch-and-Go outdoor surface dataset with four material classes and evaluates SSUF under a 10% visible-patch protocol using reconstruction and classification tables.

  • Reconstruction Evaluation: Table II reports reconstruction performance for the evaluated architectures using only 10% visible image content.The table identifies the best reconstruction results among the models.
  • Classification Evaluation: Table III reports weighted precision, recall, and F1-score for classification under the 10% visible-patch setting.The table compares classification performance across SSUF architectures.
  • Dataset Description: The Touch-and-Go dataset contains outdoor interaction images spanning Grass, Concrete, Wood, and Rock surface categories.These categories represent diverse textures and visual characteristics encountered outdoors.
  • Sparse Observation Protocol: Sparse inputs retain a random continuous patch corresponding to 10% of the original image area while masking the remainder.Images are resized to 224×224 pixels before evaluation.
  • Class-Wise Evaluation: Table IV reports precision, recall, and F1-score separately for Grass, Concrete, Wood, and Rock on the test set.This provides class-wise classification results under sparse observation.
  • Dataset Description: The dataset contains 2,921 images: 416 Grass, 1,000 Concrete, 1,000 Wood, and 505 Rock samples.The 60:20:20 split assigns 1,753 images to training and 584 each to validation and testing.

B. Image Reconstruction Performance

Under the 10% visible-patch setting, SSUF-MAE achieved the strongest reconstruction quality, while SSUF-ViT minimized reconstruction errors and SSUF-Swin performed worse on recovery.

  • 16.06 dB PSNR and 0.4501 SSIM were achieved by SSUF-MAE, the strongest reconstruction results among evaluated models.
  • 0.12861 MAE and 0.02940 MSE were obtained by SSUF-ViT, giving it the best reconstruction-error results.
  • SSUF-ConvAE produced reconstruction results close to SSUF-MAE and SSUF-ViT despite its simpler convolutional design.
  • SSUF-Swin showed lower reconstruction quality across all evaluated metrics, despite achieving the strongest material-classification performance.

D. Computational Complexity Comparison

The models differed substantially in parameter scale and inference latency, with SSUF-Swin offering the strongest complexity–classification trade-off and SSUF-MAE the fastest per-image inference.

  • 32.3 million total and 4.7 million trainable parameters made SSUF-Swin the most parameter-efficient architecture while achieving the highest classification performance.
  • 109.8 million total and 24.0 million trainable parameters made SSUF-MAE the largest evaluated model.
  • 2.536 ms per image was required by SSUF-MAE, compared with 3.865 ms for SSUF-Swin, 3.988 ms for SSUF-ConvAE, and 4.766 ms for SSUF-ViT.

F. ROC Curves of Models

ROC analysis showed strong class separability under sparse observation, especially for Grass and Swin Transformer, while confusion remained concentrated between Concrete and Wood.

  • 0.999, 0.970, 0.975, and 0.981 AUC values were achieved by Swin Transformer for Grass, Concrete, Wood, and Rock, respectively.
  • Grass achieved the highest AUC values across architectures, ranging from 0.995 to 0.999, making it the most distinguishable material category.
  • Concrete–Wood confusion was the primary classification error under severe information loss, while Swin Transformer produced the fewest overall misclassifications.
  • The results support the paper-level finding that useful material cues remain learnable from highly sparse inputs, particularly for classification.
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