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Found 23 papers, 6 papers with code
Synthesizing Training Images for Boosting Human 3D Pose Estimation
Human 3D pose estimation from a single image is a challenging task with numerous applications.
A Holistic Approach for Data-Driven Object Cutout
We show that the resulting P-maps may be used to evaluate how likely a rectangle proposal is to contain an instance of the class, and further process good proposals to produce an accurate object cutout mask.
Optimal Structured Light à La Carte
We consider the problem of automatically generating sequences of structured-light patterns for active stereo triangulation of a static scene.
Steady-state Non-Line-of-Sight Imaging
Relying on consumer color image sensors, with high fill factor, high quantum efficiency and low read-out noise, we demonstrate high-fidelity color NLOS imaging for scene configurations tackled before with picosecond time resolution.
Fast Interactive Object Annotation with Curve-GCN
Our model runs at 29. 3ms in automatic, and 2. 6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++.
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.
Ranked #4 on
Single-View 3D Reconstruction
on ShapeNet
Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research.
Auto-Tuning Structured Light by Optical Stochastic Gradient Descent
We consider the problem of optimizing the performance of an active imaging system by automatically discovering the illuminations it should use, and the way to decode them.
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties.
Learning Deformable Tetrahedral Meshes for 3D Reconstruction
We introduce Deformable Tetrahedral Meshes (DefTet) as a particular parameterization that utilizes volumetric tetrahedral meshes for the reconstruction problem.
DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers.
Extracting Triangular 3D Models, Materials, and Lighting From Images
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations.
Ranked #2 on
Depth Prediction
on Stanford-ORB
NARRATE: A Normal Assisted Free-View Portrait Stylizer
We extend Total Relighting to fix this problem by unifying its multi-view input normal maps with the physical face model.
Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion
In this work, we propose a neural approach that estimates the 5D HDR light field from a single image, and a differentiable object insertion formulation that enables end-to-end training with image-based losses that encourage realism.
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident.
HumanGen: Generating Human Radiance Fields with Explicit Priors
With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation.
Relightable Neural Human Assets from Multi-view Gradient Illuminations
UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks.
Neural Fields Meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion.
Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion.
Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction
The core of our method is to put the object nearby diffuse walls and augment the LOS scan in the front view with the NLOS scans from the surrounding walls, which serve as virtual ``mirrors'' to trap lights toward the object.
Flexible Isosurface Extraction for Gradient-Based Mesh Optimization
This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative modeling, and inverse physics.
Towards Viewpoint Robustness in Bird's Eye View Segmentation
We introduce a technique for novel view synthesis and use it to transform collected data to the viewpoint of target rigs, allowing us to train BEV segmentation models for diverse target rigs without any additional data collection or labeling cost.
4D Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes
We consider the problem of novel view synthesis (NVS) for dynamic scenes.
