REVIEW 4 cited by
DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This manual is intended to provide a detailed description of the DIML/CVL RGB-D dataset. This dataset is comprised of 2M color images and their corresponding depth maps from a great variety of natural indoor and outdoor scenes. The indoor dataset was constructed using the Microsoft Kinect v2, while the outdoor dataset was built using the stereo cameras (ZED stereo camera and built-in stereo camera). Table I summarizes the details of our dataset, including acquisition, processing, format, and toolbox. Refer to Section II and III for more details.
Forward citations
Cited by 4 Pith papers
-
Breaking the Horizontal Prior: From Long-Tailed Orientation Bias to Roll-Robust Monocular Depth Estimation
Auxiliary rotation-stable depth cues, added during fine-tuning, reduce monocular depth-error degradation under camera roll across five benchmarks.
-
DepthART: Scaling Foundation Monocular Depth to Tiny Models
DepthART transfers foundation-style monocular depth to 6-33M-parameter models via bias-resistant data sampling and frozen-encoder camera-conditioned metric fine-tuning, reaching near-foundation zero-shot accuracy at r...
-
Collaborative Learning for Unsupervised Multimodal Remote Sensing Image Registration: Integrating Self-Supervision and MIM-Guided Diffusion-Based Image Translation
An unsupervised collaborative training framework with MIM-guided diffusion translation and pseudo-label distillation reaches competitive or better cross-modal registration accuracy on five remote sensing datasets than...
-
Depth Anything at Any Condition
A fine-tuned Depth Anything V2 model using perturbation consistency and spatial distance constraints improves monocular depth estimation under adverse conditions without any labeled data.
Discussion (0). Sign in to comment.