We reconstruct clothed human avatars from sparse RGB images. MExECON extends the single-view method ECON (CVPR 2023) to arbitrary numbers of viewpoints, improving geometry and pose estimation without requiring network retraining.

ShinyNeRF advances NeRF-based 3D digitization of specular surfaces by phisically modeling isotropic and anisotropic reflections based on interpretable material/geometric parameters (normals, tangents, ASG anisotropy), enabling anisotropic material editing.

This new dataset comprises multi-view satellite images (PAN, RGB), corresponding vegetation and shadow masks, bundle-adjusted RPC camera models and ground-truth DSMs for 702 different geographic areas of 500x500 m each across three different US cities.

We evaluate the fidelity and realism of different architectural variations of a latent diffusion model, which is used to generate RGB aerial images conditioned to semantic maps.

We generate synthetic 2D chest CT imagery using a conditional latent diffusion model guided by bounding-box masks and attribute embeddings. We analyze how well the model controls lung nodule placement and characteristics, highlighting strengths and biases.

This paper reviews VaxNeRF. NeRF-based rendering is accelerated by restricting sampling to the visual hull, the maximal volume consistent with multi-view object silhouettes.

Despite the important differences between optical and synthetic aperture radar (SAR) image formation models, we show that radiance fields can be extended to radar images.

EO-NeRF is extended to handle high-res panchromatic (PAN) and low-res multispectral (MS) inputs, eliminating the need for separate pansharpening. The resulting model can render pansharpened image surrogates with high-res color information for each input viewpoint.

We propose a generic regularization framework for NeRF based on differential geometry that outperforms previous state-of-the-art methods with only three input views. We compare our approach with RegNeRF (CVPR 2022).

We present EO-NeRF, that reveals scene geometry from multi-date satellite images with an unprecedented level of detail. We propose a geometrically consistent shadow model and a radiometric decomposition of the scene adapted to pansharpened satellite images.

This thesis investigates 3D reconstruction from collections of high-resolution satellite images. The first part examines the mathematical modeling of satellite acquisition geometry, and the second part explores several applications of multi-image remote sensing.

We evaluate the performance of the deep learning architectures PSM (CVPR 2018) and HSM (CVPR 2019) for disparity estimation on multiple pairs of high-resolution satellite images.

Sat-NeRF is the first work in neural rendering for multi-date satellite images to demonstrate quantitatively convincing results in terms of surface reconstruction.

We emulate a perfect sensor to generate a single image from a fragmented push-frame strip. The resulting product simplifies large-scale 3D modeling from push-frame imagery.

We propose a generic bundle adjustment method for satellite multi-view stereo pipelines. The RPC camera models of the input views are refined with a rotation that compensates localization errors related to the attitude angles encoding the satellite orientation.

The RPC camera models of a time series of SkySat acquisitions are refined and used to compute a surface model for each date, which is used to measure the stockpile volume.

We describe a terrain-independent algorithm to accurately derive the RPC camera model linking a set of 3D-2D point correspondences based on a regularized least squares fit.

This work investigates and compares different relative geolocation correction techniques for multi-view stereo pipelines for satellite images. We assess the impact on the output geometry.

We present a deep learning method to predict extrinsic (tilt and roll) and intrinsic (focal length and radial distortion) parameters from a single image. We use a parameterization that is better suited for learning than directly predicting the camera parameters.