Search Results for author:
Found 7 papers, 3 papers with code
SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation
We present a novel approach for digitizing real-world objects by estimating their geometry, material properties, and environmental lighting from a set of posed images with fixed lighting.
Enhancing Neural Rendering Methods with Image Augmentations
We found that introducing image augmentations during training presents challenges such as geometric and photometric inconsistencies for learning NRMs from images.
Re-ReND: Real-time Rendering of NeRFs across Devices
Re-ReND is designed to achieve real-time performance by converting the NeRF into a representation that can be efficiently processed by standard graphics pipelines.
SegNeRF: 3D Part Segmentation with Neural Radiance Fields
The predicted semantic fields allow SegNeRF to achieve an average mIoU of $\textbf{30. 30%}$ for 2D novel view segmentation, and $\textbf{37. 46%}$ for 3D part segmentation, boasting competitive performance against point-based methods by using only a few posed images.
PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement
We integrate residual GCNs in a two-stage 3D object detection pipeline, where 3D object proposals are refined using a novel graph representation.
Ranked #14 on
3D Object Detection
on KITTI Cars Hard
Efficient Bird Eye View Proposals for 3D Siamese Tracking
Successively, we refine our selection of 3D object candidates by exploiting the similarity capability of a 3D Siamese network.
Leveraging Shape Completion for 3D Siamese Tracking
We design a Siamese tracker that encodes model and candidate shapes into a compact latent representation.
