Search Results for author:
Found 36 papers, 24 papers with code
SplatTalk: 3D VQA with Gaussian Splatting
Language-guided 3D scene understanding is important for advancing applications in robotics, AR/VR, and human-computer interaction, enabling models to comprehend and interact with 3D environments through natural language.
Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks.
No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images
We utilize the reconstructed 3D Gaussians for novel view synthesis and pose estimation tasks and propose a two-stage coarse-to-fine pipeline for accurate pose estimation.
DepthSplat: Connecting Gaussian Splatting and Depth
Gaussian splatting and single/multi-view depth estimation are typically studied in isolation.
WildGaussians: 3D Gaussian Splatting in the Wild
While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds.
OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation
However, this flexibility comes with a trade-off: fully-supervised closed-set methods still outperform OVS methods on base classes, that is on classes on which they have been explicitly trained.
Ranked #5 on
Open Vocabulary Semantic Segmentation
on ADE20K-150
3D Instance Segmentation
3D Open-Vocabulary Instance Segmentation
+8
NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild
Neural Radiance Fields (NeRFs) have shown remarkable success in synthesizing photorealistic views from multi-view images of static scenes, but face challenges in dynamic, real-world environments with distractors like moving objects, shadows, and lighting changes.
3D Neural Edge Reconstruction
Real-world objects and environments are predominantly composed of edge features, including straight lines and curves.
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models
Hence, with this paper, we aim to chart a course for future research that explores and expands the capabilities of 3D-LLMs in understanding and interacting with the complex 3D world.
NeRF in Robotics: A Survey
Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields.
Renovating Names in Open-Vocabulary Segmentation Benchmarks
Names are essential to both human cognition and vision-language models.
OpenSUN3D: 1st Workshop Challenge on Open-Vocabulary 3D Scene Understanding
This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023.
Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels
Therefore, we explore the use of image segmentation foundation models to automatically generate training labels for 3D segmentation.
Ternary-Type Opacity and Hybrid Odometry for RGB NeRF-SLAM
In this work, we address the challenge of deploying Neural Radiance Field (NeRFs) in Simultaneous Localization and Mapping (SLAM) under the condition of lacking depth information, relying solely on RGB inputs.
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT).
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM
Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM.
OpenScene: 3D Scene Understanding with Open Vocabularies
Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision.
Ranked #7 on
3D Open-Vocabulary Instance Segmentation
on Replica
3D Open-Vocabulary Instance Segmentation
3D Semantic Segmentation
+1
FastHuman: Reconstructing High-Quality Clothed Human in Minutes
We propose an approach for optimizing high-quality clothed human body shapes in minutes, using multi-view posed images.
DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models
Scene extrapolation -- the idea of generating novel views by flying into a given image -- is a promising, yet challenging task.
Ranked #1 on
Perpetual View Generation
on LHQ
3D Textured Shape Recovery with Learned Geometric Priors
3D textured shape recovery from partial scans is crucial for many real-world applications.
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Motivated by recent advances in the area of monocular geometry prediction, we systematically explore the utility these cues provide for improving neural implicit surface reconstruction.
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM).
Shape As Points: A Differentiable Poisson Solver
However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization.
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
At the same time, neural radiance fields have revolutionized novel view synthesis.
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images.
Dynamic Plane Convolutional Occupancy Networks
To further exploit translational equivariance, convolutional neural networks are applied to process the plane features.
Ranked #4 on
3D Reconstruction
on ShapeNet
Convolutional Occupancy Networks
Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction.
DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing
We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function.
A Deep Framework for Bone Age Assessment based on Finger Joint Localization
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks.
A Hybrid SLAM and Object Recognition System for Pepper Robot
Providing robust solutions for the tasks such as indoor environment mapping, self-localisation and object recognition are essential to make the robots to be more autonomous, hence, more human-like.
Robotics
PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship
Apparent personality and emotion analysis are both central to affective computing.
Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
It is well known that the accuracy of a calibration depends strongly on the choice of camera poses from which images of a calibration object are acquired.
Photometric Depth Super-Resolution
This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image.
A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues
In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.
Depth Super-Resolution Meets Uncalibrated Photometric Stereo
A novel depth super-resolution approach for RGB-D sensors is presented.
