Differentiable Point-based Inverse Rendering
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Updated
Jun 3, 2024 - Python
Differentiable Point-based Inverse Rendering
GenPercept: Diffusion Models Trained with Large Data Are Transferable Visual Models
Estimating Ground Surface Normals and Fitting Surfaces to Noisy LIDAR Point Cloud Data
Official code for FOUND: Foot Optimisation with Uncertain Normals for Surface Deformation using Synthetic Data
[BMVC 2022] IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty
Tools accompanying the 3D60 spherical panoramas dataset
Surface Normals and Curvature Estimation for Noisy Point Cloud Data
[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
Term project. A python implementation of the Basic Photometric Stereo Algorithm
Code accompanying the paper "360 Surface Regression with a Hyper-Sphere Loss", 3DV 2019
Normal Inference Module in PyTorch, IROS 2020
SNE-RoadSeg for Freespace Detection in PyTorch, ECCV 2020
A multimodal UAV assistant dataset.
Estimate surface normals from a single image; 3-stack-hourglass Architecture using Tensorflow; Reduce the Mean Angle Error down to 0.4397164234
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