Search Results for author:
Found 7 papers, 7 papers with code
Virtual Occlusions Through Implicit Depth
We instead propose an implicit model for depth and use that to predict the occlusion mask directly.
SimpleRecon: 3D Reconstruction Without 3D Convolutions
Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction.
Single Image Depth Prediction with Wavelet Decomposition
We present a novel method for predicting accurate depths from monocular images with high efficiency.
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
Learning Stereo from Single Images
We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs.
Footprints and Free Space from a Single Color Image
We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input.
Self-Supervised Monocular Depth Hints
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data.
Ranked #2 on
Monocular Depth Estimation
on VA (Virtual Apartment)
