Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 1145-1150· 0 citations· 21 references
Abstract
Recent advancements in transformer-based deep learning have significantly improved the accuracy and efficiency of single-image 3D reconstruction. The transformer-based feed-forward architecture for single-image 3D reconstruction, system integrates DINOv1 vision transformers, triplane repre-sentations and Neural Radiance Fields (NeRF) principles to generate textured 3D meshes from single RGB images. Key contributions include efficient triplane decoding, robust pre-processing with background removal (rembg) and edge de-tection, and a Gradio-based web interface enabling real-time deployment on consumer GPUs (6GB VRAM). Preliminary deployment experiments suggest that Image2Mesh is capable of near-real-time inference on high-performance GPUs while demonstrating a favorable speed–accuracy trade-off compared to traditional photogrammetry-based workflows. Image2Mesh provides a practical foundation for AR/VR, gaming, and content creation applications.
This work aims to guide future research toward robust, accurate, and certifiable 3D reconstruction systems suitable for engineering, industrial, and geospatial applications, by bridging the gap between classical photogrammetry and data-driven 3D vision.
Xin Wang, Tengfei Wang, M. Hillemann et al.· PFG – Journal of Photogramme...· 0 citations
Feed-forward 3D reconstruction models have achieved impressive performance by scaling model and dataset size, but their cost excludes most research groups and precludes edge deployment. Additionally, generating 3D supervision without sensors still relies on slow, unreliable Structure-from-Motion, as the community lacks...
Mask-based lensless imagers capture measurements using simple, compact hardware and recover images using a reconstruction algorithm. The reconstruction algorithm affects the image quality, inference speed, and computational requirements of the imaging system, often trading off image quality against computational effici...
Leyla A. Kabuli, Vasilisa Ponomarenko, Laura Waller· 0 citations
AnyGS2Mesh is presented, the first feed-forward framework for directly reconstructing 3D meshes from 3D Gaussian Splatting representations with support for arbitrary input image resolutions, and demonstrates the potential of combining Gaussian representations and feed-forward Transformer architectures for scalable 3D g...
Yuxuan Song, Fan Gao, Yi-Bo Zhao et al.· 0 citations
The task of monocular three-dimensional reconstruction represents a critical yet fundamentally ill-posed inverse problem in the field of computer vision, constrained primarily by the inherent loss of depth information during the two-dimensional perspective projection process. Existing optimization-based neural renderin...
Mei-Xia Dong, Jin-Huang Chen· 2026 2nd International Confe...· 0 citations
Recent advances in 3D content generation have demonstrated the effectiveness of optimization-based frameworks such as DreamGaussian, which combine 3D Gaussian Splatting (3DGS) with diffusion-guided Score Distillation Sampling (SDS) to efficiently synthesize 3D assets from a single image. Compared with earlier NeRF-base...
