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Found 17 papers, 10 papers with code
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation
Procedural Content Generation (PCG) is powerful in creating high-quality 3D contents, yet controlling it to produce desired shapes is difficult and often requires extensive parameter tuning.
AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos
We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation.
StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gaming, and filmmaking, etc.
MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction
This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane.
Tracking Everything in Robotic-Assisted Surgery
However, its efficacy in surgical scenarios remains untested, largely due to the lack of a comprehensive surgical tracking dataset for evaluation.
Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models
The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software.
T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF.
MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending
In this paper, we explore the potential of MPI and show that MPI can synthesize high-quality novel views of complex scenes with diverse camera distributions and view directions, which are not only limited to simple forward-facing scenes.
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors.
O$^2$-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Model
In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting model, to reconstruct complete surfaces for the hidden parts of objects.
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild
In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories.
A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo
Experimental results on ScanNet and RGB-D Scenes V2 demonstrate state-of-the-art performance of the proposed deep MVS system on multi-view depth estimation, with our proposed solver consistently improving the depth quality over both conventional and deep learning based MVS pipelines.
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo
In this work, we present a new multi-view depth estimation method that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF).
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning.
A Configuration-Space Decomposition Scheme for Learning-based Collision Checking
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution.
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction
In this paper, we present a new perspective towards image-based shape generation.
GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.
Ranked #32 on
3D Object Detection
on ScanNetV2
