Search Results for author:
Found 17 papers, 3 papers with code
Globally Optimal and Efficient Vanishing Point Estimation in Atlanta World
In Atlanta world, given a set of image lines, we aim to cluster them by the unknown-but-sought VPs whose number is unknown.
Convex Relaxation for Robust Vanishing Point Estimation in Manhattan World
To solve this SDP problem efficiently, we present a globally optimal outlier-robust iterative solver (called GlobustVP), which independently searches for one VP and its associated lines in each iteration, treating other lines as outliers.
L2COcc: Lightweight Camera-Centric Semantic Scene Completion via Distillation of LiDAR Model
Semantic Scene Completion (SSC) constitutes a pivotal element in autonomous driving perception systems, tasked with inferring the 3D semantic occupancy of a scene from sensory data.
Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding
The performance of VLA models can be improved by integrating with action chunking, a critical technique for effective control.
DynSUP: Dynamic Gaussian Splatting from An Unposed Image Pair
To overcome this challenge, we propose a method that can use only two images without prior poses to fit Gaussians in dynamic environments.
PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments
Our method is composed of three main modules to 1) map the dynamic foreground including non-rigid humans and rigid items, 2) reconstruct the static background, and 3) localize the camera.
ES-Gaussian: Gaussian Splatting Mapping via Error Space-Based Gaussian Completion
We propose ES-Gaussian, an end-to-end system using a low-altitude camera and single-line LiDAR for high-quality 3D indoor reconstruction.
Physically-Based Photometric Bundle Adjustment in Non-Lambertian Environments
Photometric bundle adjustment (PBA) is widely used in estimating the camera pose and 3D geometry by assuming a Lambertian world.
GlobalPointer: Large-Scale Plane Adjustment with Bi-Convex Relaxation
Plane adjustment (PA) is crucial for many 3D applications, involving simultaneous pose estimation and plane recovery.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search
Our strategy largely reduces the search space and can guarantee accuracy with only a few inlier samples, therefore enjoying an excellent trade-off between efficiency and robustness.
DDIT: Semantic Scene Completion via Deformable Deep Implicit Templates
It abstracts the shape prior of a category, and thus can provide constraints on the overall shape of an instance.
Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging
As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy.
Deterministic Point Cloud Registration via Novel Transformation Decomposition
Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF).
Learning To Identify Correct 2D-2D Line Correspondences on Sphere
In contrast, we propose the first approach suitable for both structured and unstructured scenes.
Learning Icosahedral Spherical Probability Map Based on Bingham Mixture Model for Vanishing Point Estimation
Based on this map, we can detect all the VPs.
Quasi-Globally Optimal and Efficient Vanishing Point Estimation in Manhattan World
In Manhattan world, given several lines in a calibrated image, we aim at clustering them by three unknown-but-sought VPs.
