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Found 3 papers, 2 papers with code
Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation
In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting.
MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation
We release MiDaS v3. 1 for monocular depth estimation, offering a variety of new models based on different encoder backbones.
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
Finally, ZoeD-M12-NK is the first model that can jointly train on multiple datasets (NYU Depth v2 and KITTI) without a significant drop in performance and achieve unprecedented zero-shot generalization performance to eight unseen datasets from both indoor and outdoor domains.
Ranked #13 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
