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ARXIV:2603.25131 · COMPUTER VISION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.25131COMPUTER VISIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEYaowen Chang · Zhen Cao · Xu Zheng · Xiaoxin Mi · Zhen Dong · arXiv
A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world…
Opportunity summary
Pain A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world applications.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world applications. However, progress in…
Panoramic semantic segmentation is pivotal for comprehensive 360° scene understanding in critical applications like autonomous driving and virtual reality. However, progress in this domain is constrained by two key challenges: the severe geometric distortions…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. DAPASS achieves state-of-the-art performances on outdoor (Cityscapes-to-DensePASS) and indoor (Stanford2D3D) benchmarks, yielding 55.04% (+2.05%) and 70.38% (+1.54%) mIoU, respectively. Code availability is flagged in…
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world…
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10.48550/arXiv.2603.25131A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world applications.
Abstract
Panoramic semantic segmentation is pivotal for comprehensive 360° scene understanding in critical applications like autonomous driving and virtual reality. However, progress in this domain is constrained by two key challenges: the severe geometric distortions inherent in panoramic projections and the prohibitive cost of dense annotation. While Unsupervised Domain Adaptation (UDA) from label-rich pinhole-camera datasets offers a viable alternative, many real-world tasks impose a stricter source-free (SFUDA) constraint where source data is inaccessible for privacy or proprietary reasons. This constraint significantly amplifies the core problems of domain shift, leading to unreliable pseudo-labels and dramatic performance degradation, particularly for minority classes. To overcome these limitations, we propose the DAPASS framework. DAPASS introduces two synergistic modules to robustly transfer knowledge without source data. First, our Panoramic Confidence-Guided Denoising (PCGD) module generates high-fidelity, class-balanced pseudo-labels by enforcing perturbation consistency and incorporating neighborhood-level confidence to filter noise. Second, a Contextual Resolution Adversarial Module (CRAM) explicitly addresses scale variance and distortion by adversarially aligning fine-grained details from high-resolution crops with global semantics from low-resolution contexts. DAPASS achieves state-of-the-art performances on outdoor (Cityscapes-to-DensePASS) and indoor (Stanford2D3D) benchmarks, yielding 55.04% (+2.05%) and 70.38% (+1.54%) mIoU, respectively.
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Dimensions overall score 7.0
PROBLEM
A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world applications. However, progress in this domain is const...
METHOD
Panoramic semantic segmentation is pivotal for comprehensive 360° scene understanding in critical applications like autonomous driving and virtual reality. However, progress in this domain is constrained by two key challenges: the severe geometric distortions inherent in panoram...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. DAPASS achieves state-of-the-art performances on outdoor (Cityscapes-to-DensePASS) and indoor (Stanford2D3D) benchmarks, yielding 55.04% (+2.05%) and 70.38% (+1.54%) mIoU, respectively. Code availability...
WHY NOW
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world applications. However, progress in this domain is constrained by two key challenges: the severe geometric distortions inherent in panoramic projections and the prohibitive cost of dense annotation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Panoramic semantic segmentation is pivotal for comprehensive 360° scene understanding in critical applications like autonomous driving and virtual reality. However, progress in this domain is constrained by two key challenges: the severe geometric distortions inherent in panoramic projections and the prohibitive cost of dense annotation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. DAPASS achieves state-of-the-art performances on outdoor (Cityscapes-to-DensePASS) and indoor (Stanford2D3D) benchmarks, yielding 55.04% (+2.05%) and 70.38% (+1.54%) mIoU, respectively. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Computer Vision moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for robust panoramic semantic segmentation that adapts models from labeled pinhole datasets to unlabeled panoramic data without access to the original source data, improving performance on challenging real-world applications.
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Computer Vision
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