CVI²

• Congrats to the winners for their great performance:
  • 1st place: Jianchuan Chen, Wentao Yi, Xu Jia and Huchuan Lu from Dalian University of Technology, China.
  • 2nd place: Lei Li, Zhizheng Liu, Weining Ren, Liudi Yang, Songyou Peng, Fangjinhua Wang and Marc Pollefeys from ETH Zurich, Switzerland.
• The competition is closed!
• The evaluation datasets have been shared with the participants.
• The evaluation phase of SHARP challenges is hosted at Codalab. Check the following pages to see the leaderboard:
  • Challenge 1 – Track 1
  • Challenge 1 – Track 2
  • Challenge 2 – Track 1
  • Challenge 2 – Track 2
• The detailed programme has been announced!
  • Prof. Angela Dai and Prof. Tolga Birdal will give live plenary talks!
  • Check this page for more information.

The 3rd SHApe Recovery from Partial textured 3D scans (SHARP) Workshop and Challenge will be held in conjunction with CVPR on June 19, 2022 (TBC). Research on data-driven 3D reconstruction of shapes has been very active in the past years thanks to the availability of large datasets of 3D models. However, current methods did not focus enough on two important aspects: (1) reconstructing full 3D objects with shape and texture at the same time, (2) reconstructing sharp edges that tend to be smoothed by 3D scanning.  The goal of this workshop is to promote the development of methods to recover a complete 3D scan from its partial acquisition. This should in turn foster the development of 3D modelling and processing techniques that exploit both geometry and texture.

This workshop will host a competition focusing on the reconstruction of full high-resolution 3D meshes from partial or noisy 3D scans and includes 2 challenges and 3 datasets:

• The first challenge consists of the recovery of textured 3D scans from partial acquisition. It involoves 2 tracks:
  • Track 1: Recovering textured human body scans from partial acquisitions. The dataset used in this scope is the 3DBodyTex.v2 dataset, containing 2500 textured 3D scans. It is an extended version of the original 3DBodyTex.v1 dataset, first published in the 2018 International Conference on 3D Computer Vision, 3DV 2018.
  • Track 2: Recovering textured object scans from partial acquisitions. It involves the recovery of generic object scans from the 3DObjTex.v1 dataset, which is a subset from the ViewShape online repository of 3D scans. This dataset contains over 2000 various generic objects with different levels of complexity in texture and geometry.

• The second challenge focuses on the recovery of fine object details in the form of sharp edges from noisy sparse scans with smooth edges. The CC3D-PSE dataset which is a new version of the CC3D dataset, introduced at the 2020 IEEE International Conference on Image Processing (ICIP), will be used for this purpose. It contains over 50k pairs of CAD models and their corresponding 3D scans. Each pair of scan and CAD model is annotated with parametric sharp edges. Given a 3D scan with smooth edges, the goal is to reconstruct the corresponding CAD model as a triangular mesh, with sharp edges approximating the ground-truth sharp edges. The second challenge invovles 2 tracks:
  • Track 1: Recovering linear sharp edges. A subset of the CC3D-PSE dataset is considered in this track which includes only linear sharp edges.
  • Track 2:  Recovering sharp edges as linear, circular, and spline segments. The whole CC3D-PSE will be used in this track.