NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces

• URL: http://arxiv.org/abs/2506.05815v1
• Date: Fri, 06 Jun 2025 07:27:15 GMT
• Title: NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces
• Authors: Pierluigi Zama Ramirez, Fabio Tosi, Luigi Di Stefano, Radu Timofte, Alex Costanzino, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Zhe Zhang, Yang Yang, Wu Chen, Anlong Ming, Mingshuai Zhao, Mengying Yu, Shida Gao, Xiangfeng Wang, Feng Xue, Jun Shi, Yong Yang, Yong A, Yixiang Jin, Dingzhe Li, Aryan Shukla, Liam Frija-Altarac, Matthew Toews, Hui Geng, Tianjiao Wan, Zijian Gao, Qisheng Xu, Kele Xu, Zijian Zang, Jameer Babu Pinjari, Kuldeep Purohit, Mykola Lavreniuk, Jing Cao, Shenyi Li, Kui Jiang, Junjun Jiang, Yong Huang,
• Abstract summary: This paper reports on the NTIRE 2025 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2025.<n>This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces.
• Score: 103.74244735167764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper reports on the NTIRE 2025 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2025. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 177 registered participants. In the final testing stage, 4 and 4 participating teams submitted their models and fact sheets for the two tracks.

Related papers

• NTIRE 2025 Image Shadow Removal Challenge Report [125.80015208285496]
  This work examines the findings of the NTIRE 2025 Shadow Removal Challenge.<n>A total of 306 participants have registered, with 17 teams successfully submitting their solutions.<n>The challenge had two evaluation tracks: one focusing on reconstruction fidelity and the other on visual perception through a user study.
  arXiv  Detail & Related papers  (2025-06-18T14:58:49Z)
• NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment [146.76913448156176]
  This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment.<n>The aim of this challenge is to address the fine-grained quality assessment of text-to-image generation models.
  arXiv  Detail & Related papers  (2025-05-22T07:12:36Z)
• NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results [159.15538432295656]
  The NTIRE 2025 image super-resolution ($times$4) challenge is one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025.<n>The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through bicubic downsampling with a $times$4 scaling factor.<n>A total of 286 participants registered for the competition, with 25 teams submitting valid entries.
  arXiv  Detail & Related papers  (2025-04-20T12:08:22Z)
• NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results [162.7095344078484]
  We present an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring.<n>The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring.<n>We anticipate that this challenge will drive further advancements in event-based vision research.
  arXiv  Detail & Related papers  (2025-04-16T18:06:16Z)
• The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report [170.81876816944754]
  The NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR) aims to advance the development of models that optimize key computational metrics.<n>This paper meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
  arXiv  Detail & Related papers  (2025-04-14T20:18:21Z)
• Technical Report for CVPR 2024 WeatherProof Dataset Challenge: Semantic Segmentation on Paired Real Data [9.128113804878959]
  This challenge aims at semantic segmentation of images degraded by various degrees of weather from all around the world. We introduced a pre-trained large-scale vision foundation model: InternImage, and trained it using images with different levels of noise. As a result, we achieved 2nd place in the challenge with 45.1 mIOU and fewer submissions than the other winners.
  arXiv  Detail & Related papers  (2024-06-09T17:08:07Z)

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.