Shape, light, and material decomposition from images using monte carlo rendering and denoising
Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D
scenes from multi-view images. Most methods rely on simple rendering algorithms: pre …
scenes from multi-view images. Most methods rely on simple rendering algorithms: pre …
Towards better gradient consistency for neural signed distance functions via level set alignment
Neural signed distance functions (SDFs) have shown remarkable capability in representing
geometry with details. However, without signed distance supervision, it is still a challenge to …
geometry with details. However, without signed distance supervision, it is still a challenge to …
Diff-lfd: Contact-aware model-based learning from visual demonstration for robotic manipulation via differentiable physics-based simulation and rendering
Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …
new skills through expert observation, significantly mitigating the need for laborious manual …
Recursive control variates for inverse rendering
We present a method for reducing errors---variance and bias---in physically based
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …
Unsupervised inference of signed distance functions from single sparse point clouds without learning priors
It is vital to infer signed distance functions (SDFs) from 3D point clouds. The latest methods
rely on generalizing the priors learned from large scale supervision. However, the learned …
rely on generalizing the priors learned from large scale supervision. However, the learned …
Gridpull: Towards scalability in learning implicit representations from 3d point clouds
Learning implicit representations has been a widely used solution for surface reconstruction
from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a …
from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a …
Learning neural implicit through volume rendering with attentive depth fusion priors
Learning neural implicit representations has achieved remarkable performance in 3D
reconstruction from multi-view images. Current methods use volume rendering to render …
reconstruction from multi-view images. Current methods use volume rendering to render …
Coordinate quantized neural implicit representations for multi-view reconstruction
In recent years, huge progress has been made on learn-ing neural implicit representations
from multi-view images for 3D reconstruction. As an additional input complement-ing …
from multi-view images for 3D reconstruction. As an additional input complement-ing …
Differentiable rendering of neural sdfs through reparameterization
We present a method to automatically compute correct gradients with respect to geometric
scene parameters in neural SDF renderers. Recent physically-based differentiable …
scene parameters in neural SDF renderers. Recent physically-based differentiable …
Humans as light bulbs: 3d human reconstruction from thermal reflection
The relatively hot temperature of the human body causes people to turn into long-wave
infrared light sources. Since this emitted light has a larger wavelength than visible light …
infrared light sources. Since this emitted light has a larger wavelength than visible light …