2,043 research outputs found
GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation
We present a learning-based method, namely GeoUDF,to tackle the long-standing
and challenging problem of reconstructing a discrete surface from a sparse
point cloud.To be specific, we propose a geometry-guided learning method for
UDF and its gradient estimation that explicitly formulates the unsigned
distance of a query point as the learnable affine averaging of its distances to
the tangent planes of neighboring points on the surface. Besides,we model the
local geometric structure of the input point clouds by explicitly learning a
quadratic polynomial for each point. This not only facilitates upsampling the
input sparse point cloud but also naturally induces unoriented normal, which
further augments UDF estimation. Finally, to extract triangle meshes from the
predicted UDF we propose a customized edge-based marching cube module. We
conduct extensive experiments and ablation studies to demonstrate the
significant advantages of our method over state-of-the-art methods in terms of
reconstruction accuracy, efficiency, and generality. The source code is
publicly available at https://github.com/rsy6318/GeoUDF
A Sparse Multi-Scale Algorithm for Dense Optimal Transport
Discrete optimal transport solvers do not scale well on dense large problems
since they do not explicitly exploit the geometric structure of the cost
function. In analogy to continuous optimal transport we provide a framework to
verify global optimality of a discrete transport plan locally. This allows
construction of an algorithm to solve large dense problems by considering a
sequence of sparse problems instead. The algorithm lends itself to being
combined with a hierarchical multi-scale scheme. Any existing discrete solver
can be used as internal black-box.Several cost functions, including the noisy
squared Euclidean distance, are explicitly detailed. We observe a significant
reduction of run-time and memory requirements.Comment: Published "online first" in Journal of Mathematical Imaging and
Vision, see DO
ACRMiner: An Incremental Approach for Finding Dense and Sparse Rectangular Regions from a 2D Interval Dataset
In many applications, transactions are associated with intervals related to time, temperature, humidity or other similar measures. The term "2D interval data" or "rectangle data" is used when there are two connected intervals with each transaction. Two connected intervals give rise to a rectangle. The rectangles may overlap producing regions with different density values. The density value or support of a region is the number of rectangles that contain it. A region is closed if its density is strictly bigger than any region properly containing it. For rectangle dataset, these regions are rectangular in shape.In this paper an algorithm named ACRMiner has been proposed that takes as input a sequence of rectangles and computes all closed overlapping rectangles and their density values. The algorithm is incremental and thus is suitable for dynamic environment. Depending on an input threshold the regions can be classified as dense and sparse.Here a tree-based data structure named as ACR-Tree is used. The method has been implemented and tested on synthetic and real-life datasets and results have been reported. Few applications of this algorithm have been discussed. The worst-case time complexity the algorithmis O(n5) where n is the number of input rectangles
Code as Reward: Empowering Reinforcement Learning with VLMs
Pre-trained Vision-Language Models (VLMs) are able to understand visual
concepts, describe and decompose complex tasks into sub-tasks, and provide
feedback on task completion. In this paper, we aim to leverage these
capabilities to support the training of reinforcement learning (RL) agents. In
principle, VLMs are well suited for this purpose, as they can naturally analyze
image-based observations and provide feedback (reward) on learning progress.
However, inference in VLMs is computationally expensive, so querying them
frequently to compute rewards would significantly slowdown the training of an
RL agent. To address this challenge, we propose a framework named Code as
Reward (VLM-CaR). VLM-CaR produces dense reward functions from VLMs through
code generation, thereby significantly reducing the computational burden of
querying the VLM directly. We show that the dense rewards generated through our
approach are very accurate across a diverse set of discrete and continuous
environments, and can be more effective in training RL policies than the
original sparse environment rewards
- …