Search Results for author:
Found 6 papers, 5 papers with code
LDM3D-VR: Latent Diffusion Model for 3D VR
Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions.
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.
LDM3D: Latent Diffusion Model for 3D
This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images from text prompts.
Monocular Visual-Inertial Depth Estimation
We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in inverse RMSE with dense scale alignment relative to performing just global alignment alone.
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)
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
In this paper, we address the problem of fast depth estimation on embedded systems.
