DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning

In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency. Such approaches lack robustness and are unable to generalize to challenging domains such as nighttime scenes or adverse weather conditions where assumptions about photometric consistency break down. We propose DeFeat-Net (Depth & Feature network), an approach to simultaneously learn a cross-domain dense feature representation, alongside a robust depth-estimation framework based on warped feature consistency. The resulting feature representation is learned in an unsupervised manner with no explicit ground-truth correspondences required. We show that within a single domain, our technique is comparable to both the current state of the art in monocular depth estimation and supervised feature representation learning. However, by simultaneously learning features, depth and motion, our technique is able to generalize to challenging domains, allowing DeFeat-Net to outperform the current state-of-the-art with around 10% reduction in all error measures on more challenging sequences such as nighttime driving

AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization

Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to the lack of explicit 3D supervision and imprecise camera poses, resulting in suboptimal outcomes. To tackle these issues, we propose AltNeRF -- a novel framework designed to create resilient NeRF representations using self-supervised monocular depth estimation (SMDE) from monocular videos, without relying on known camera poses. SMDE in AltNeRF masterfully learns depth and pose priors to regulate NeRF training. The depth prior enriches NeRF's capacity for precise scene geometry depiction, while the pose prior provides a robust starting point for subsequent pose refinement. Moreover, we introduce an alternating algorithm that harmoniously melds NeRF outputs into SMDE through a consistence-driven mechanism, thus enhancing the integrity of depth priors. This alternation empowers AltNeRF to progressively refine NeRF representations, yielding the synthesis of realistic novel views. Additionally, we curate a distinctive dataset comprising indoor videos captured via mobile devices. Extensive experiments showcase the compelling capabilities of AltNeRF in generating high-fidelity and robust novel views that closely resemble reality

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Geology and Physical Properties Investigations by the InSight Lander

Although not the prime focus of the InSight mission, the near-surface geology and physical properties investigations provide critical information for both placing the instruments (seismometer and heat flow probe with mole) on the surface and for understanding the nature of the shallow subsurface and its effect on recorded seismic waves. Two color cameras on the lander will obtain multiple stereo images of the surface and its interaction with the spacecraft. Images will be used to identify the geologic materials and features present, quantify their areal coverage, help determine the basic geologic evolution of the area, and provide ground truth for orbital remote sensing data. A radiometer will measure the hourly temperature of the surface in two spots, which will determine the thermal inertia of the surface materials present and their particle size and/or cohesion. Continuous measurements of wind speed and direction offer a unique opportunity to correlate dust devils and high winds with eolian changes imaged at the surface and to determine the threshold friction wind stress for grain motion on Mars. During the first two weeks after landing, these investigations will support the selection of instrument placement locations that are relatively smooth, flat, free of small rocks and load bearing. Soil mechanics parameters and elastic properties of near surface materials will be determined from mole penetration and thermal conductivity measurements from the surface to 3–5 m depth, the measurement of seismic waves during mole hammering, passive monitoring of seismic waves, and experiments with the arm and scoop of the lander (indentations, scraping and trenching). These investigations will determine and test the presence and mechanical properties of the expected 3–17 m thick fragmented regolith (and underlying fractured material) built up by impact and eolian processes on top of Hesperian lava flows and determine its seismic properties for the seismic investigation of Mars’ interior

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 182, July 1978

This bibliography lists 165 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1978

LANDSAT-4 Science Characterization Early Results. Volume 3, Part 2: Thematic Mapper (TM)

The calibration of the LANDSAT 4 thematic mapper is discussed as well as the atmospheric, radiometric, and geometric accuracy and correction of data obtained with this sensor. Methods are given for assessing TM band to band registration