Shape2.5D: A Dataset of Texture-Less Surfaces for Depth and Normals Estimation
Reconstructing texture-less surfaces poses unique challenges in computer vision, primarily due to the lack of specialized datasets that cater to the nuanced needs of depth and normals estimation in the absence of textural information. We introduce “Shape2.5D,” a novel, large-sc...
Saved in:
| Main Authors: | Muhammad Saif Ullah Khan, Sankalp Sinha, Didier Stricker, Marcus Liwicki, Muhammad Zeshan Afzal |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10745503/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Attention-CARU With Texture-Temporal Network for Video Depth Estimation
by: Sio-Kei Im, et al.
Published: (2025-01-01) -
High-Precision Depth Estimation Networks Using Low-Resolution Depth and RGB Image Sensors for Low-Cost Mixed Reality Glasses
by: Wei-Jong Yang, et al.
Published: (2025-05-01) -
Cattle weight estimation using 2D side-view images and estimated depth-based 3D modeling
by: Guilherme Botazzo Rozendo, et al.
Published: (2025-12-01) -
Dataset Generation Process for Enhancing Depth Estimation Network in Autonomous Driving
by: Jinsu Ha, et al.
Published: (2024-01-01) -
Arithmetically Fast Position Transformation for View Synthesis and Depth Estimation
by: Krzysztof Wegner, et al.
Published: (2025-01-01)