Search Results for author:
Found 12 papers, 4 papers with code
Language models scale reliably with over-training and on downstream tasks
We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters.
Robust Self-Supervised Extrinsic Self-Calibration
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely.
Towards Zero-Shot Scale-Aware Monocular Depth Estimation
Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions.
NeRFuser: Large-Scale Scene Representation by NeRF Fusion
A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small neural nets instead of collections of images.
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information.
Neural Camera Models
Modern computer vision has moved beyond the domain of internet photo collections and into the physical world, guiding camera-equipped robots and autonomous cars through unstructured environments.
Depth Field Networks for Generalizable Multi-view Scene Representation
Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matching.
Self-Supervised Camera Self-Calibration from Video
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams.
Full Surround Monodepth from Multiple Cameras
In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs.
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion
Self-supervised learning has emerged as a powerful tool for depth and ego-motion estimation, leading to state-of-the-art results on benchmark datasets.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Examining the Impact of Blur on Recognition by Convolutional Networks
We investigate the extent to which this degradation is due to the mismatch between training and input image statistics.
