Search Results for author:
Found 24 papers, 7 papers with code
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching
Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play a core contribution to their success.
Para-Lane: Multi-Lane Dataset Registering Parallel Scans for Benchmarking Novel View Synthesis
To evaluate end-to-end autonomous driving systems, a simulation environment based on Novel View Synthesis (NVS) techniques is essential, which synthesizes photo-realistic images and point clouds from previously recorded sequences under new vehicle poses, particularly in cross-lane scenarios.
Splatter-360: Generalizable 360$^{\circ}$ Gaussian Splatting for Wide-baseline Panoramic Images
Wide-baseline panoramic images are frequently used in applications like VR and simulations to minimize capturing labor costs and storage needs.
TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting
Physically Based Rendering (PBR) materials play a crucial role in modern graphics, enabling photorealistic rendering across diverse environment maps.
DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes
This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes.
Surfel-based Gaussian Inverse Rendering for Fast and Relightable Dynamic Human Reconstruction from Monocular Video
Efficient and accurate reconstruction of a relightable, dynamic clothed human avatar from a monocular video is crucial for the entertainment industry.
XLD: A Cross-Lane Dataset for Benchmarking Novel Driving View Synthesis
To address this, this paper presents a synthetic dataset for novel driving view synthesis evaluation, which is specifically designed for autonomous driving simulations.
VDG: Vision-Only Dynamic Gaussian for Driving Simulation
Dynamic Gaussian splatting has led to impressive scene reconstruction and image synthesis advances in novel views.
OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding
To ensure robust feature presentation and 3D point-level understanding, we first employ SAM masks without cross-frame associations to train instance features with 3D consistency.
HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes
The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings.
TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization
We propose an optimal viewpoint selection strategy, that finds the most miniature set of viewpoints covering all the faces of a mesh.
GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time
Specifically, we design a novel joint learning framework that consists of an Iterative Pose Optimization Network (IPO-Net) and a Generalizable 3D-Gaussians (G-3DG) model.
GVA: Reconstructing Vivid 3D Gaussian Avatars from Monocular Videos
In this paper, we present a novel method that facilitates the creation of vivid 3D Gaussian avatars from monocular video inputs (GVA).
A Pedestrian is Worth One Prompt: Towards Language Guidance Person Re-Identification
Whiteness the impressive capabilities in multimodal understanding of Vision Language Foundation Model CLIP a recent two-stage CLIP-based method employs automated prompt engineering to obtain specific textual labels for classifying pedestrians.
TexOct: Generating Textures of 3D Models with Octree-based Diffusion
This model gradually reduces the texture noise on the octree nodes resulting in the restoration of fine texture.
GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
Our method achieves state-of-the-art performance in both relighting and novel view synthesis tasks among the recently proposed inverse rendering methods while achieving real-time rendering.
DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes
Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand.
Understanding In-Context Learning from Repetitions
This paper explores the elusive mechanism underpinning in-context learning in Large Language Models (LLMs).
Digging into Depth Priors for Outdoor Neural Radiance Fields
However, the shape-radiance ambiguity of radiance fields remains a challenge, especially in the sparse viewpoints setting.
MapNeRF: Incorporating Map Priors into Neural Radiance Fields for Driving View Simulation
The key insight is that map information can be utilized as a prior to guiding the training of the radiance fields with uncertainty.
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
This paper presents a new method that uses scores provided by humans instead of pairwise preferences to improve the feedback efficiency of interactive reinforcement learning.
NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering
This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces using pre-integrated rendering from multi-view images or video.
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
This setting results in two distinct domain gaps: scenarios and sensors, making it difficult to analyze and evaluate the method accurately.
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space
Current evaluation of an NMT system is usually built upon a heuristic decoding algorithm (e. g., beam search) and an evaluation metric assessing similarity between the translation and golden reference.
