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Found 9 papers, 4 papers with code
GauStudio: A Modular Framework for 3D Gaussian Splatting and Beyond
We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline.
TIFace: Improving Facial Reconstruction through Tensorial Radiance Fields and Implicit Surfaces
This report describes the solution that secured the first place in the "View Synthesis Challenge for Human Heads (VSCHH)" at the ICCV 2023 workshop.
TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning
Further, we propose TopoMLP, a simple yet high-performance pipeline for driving topology reasoning.
Ranked #3 on
3D Lane Detection
on OpenLane-V2 val
The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge
We present the 1st-place solution of OpenLane Topology in Autonomous Driving Challenge.
Structured Epipolar Matcher for Local Feature Matching
Local feature matching is challenging due to textureless and repetitive patterns.
Adaptive Spot-Guided Transformer for Consistent Local Feature Matching
To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture.
Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View
Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation.
Visual Recognition-Driven Image Restoration for Multiple Degradation With Intrinsic Semantics Recovery
Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images.
DETRDistill: A Universal Knowledge Distillation Framework for DETR-families
Next, we propose a target-aware feature distillation to help the student model learn from the object-centric features of the teacher model.
