Search Results for author:
Found 31 papers, 21 papers with code
Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning
A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images.
Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
The 3D-ED-GAN is a 3D convolutional neural network trained with a generative adversarial paradigm to fill missing 3D data in low-resolution.
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results.
Ranked #1 on
3D Semantic Instance Segmentation
on ScanNetV1
Learning to Prune Filters in Convolutional Neural Networks
Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way.
Recurrent Slice Networks for 3D Segmentation of Point Clouds
The key component of the RSNet is a lightweight local dependency module.
Ranked #46 on
Semantic Segmentation
on S3DIS
Depth-aware CNN for RGB-D Segmentation
Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure.
Ranked #6 on
Semantic Segmentation
on Stanford2D3D - RGBD
Stochastic Dynamics for Video Infilling
In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video.
Efficient Multi-Domain Dictionary Learning with GANs
In this paper, we propose the multi-domain dictionary learn- ing (MDDL) to make dictionary learning-based classification more robust to data representing in different domains.
3DN: 3D Deformation Network
Given such a source 3D model and a target which can be a 2D image, 3D model, or a point cloud acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms the source model to resemble the target.
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Reconstructing 3D shapes from single-view images has been a long-standing research problem.
Ranked #1 on
Single-View 3D Reconstruction
on ShapeNetCore
Salient Building Outline Enhancement and Extraction Using Iterative L0 Smoothing and Line Enhancing
Also, we propose to create building masks from semantic segmentation using an encoder-decoder network.
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion
Such a transformation enables CFCNet to predict features and reconstruct data of missing depth measurements according to their corresponding, transformed RGB features.
Grid-GCN for Fast and Scalable Point Cloud Learning
Compared with popular sampling methods such as Farthest Point Sampling (FPS) and Ball Query, CAGQ achieves up to 50X speed-up.
Scene Completeness-Aware Lidar Depth Completion for Driving Scenario
Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as KITTI, do not provide groundtruth for upper areas.
Geometry-Aware Instance Segmentation with Disparity Maps
Mask regression is based on 2D, 2. 5D, and 3D ROI using the pseudo-lidar and image-based representations.
Ranked #16 on
Instance Segmentation
on Cityscapes val
(using extra training data)
Accurate 3D Facial Geometry Prediction by Multi-Task, Multi-Modal, and Multi-Representation Landmark Refinement Network
This work focuses on complete 3D facial geometry prediction, including 3D facial alignment via 3D face modeling and face orientation estimation using the proposed multi-task, multi-modal, and multi-representation landmark refinement network (M$^3$-LRN).
Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices
This work focuses on the analysis that whether 3D face models can be learned from only the speech inputs of speakers.
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.
Ranked #1 on
Face Alignment
on AFLW
Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances.
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection
Finally, the probability of occupancy is also integrated into a proposal refinement module to generate the final bounding boxes.
Ranked #2 on
3D Object Detection
on KITTI Cars Moderate
Toward Practical Monocular Indoor Depth Estimation
The majority of prior monocular depth estimation methods without groundtruth depth guidance focus on driving scenarios.
Ranked #1 on
Monocular Depth Estimation
on VA (Virtual Apartment)
Point-NeRF: Point-based Neural Radiance Fields
Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field.
Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?
This work digs into a root question in human perception: can face geometry be gleaned from one's voices?
Ranked #1 on
3D Face Modelling
on Voxceleb-3D
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Further, we apply the proposed framework to current SOTA multi-agent multi-modal forecasting systems as a plugin module, which enables the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal trajectory forecasting task; 2) rank the multiple predictions and select the optimal one based on the estimated uncertainty.
Aware of the History: Trajectory Forecasting with the Local Behavior Data
In this work, we re-introduce this information as a new type of input data for trajectory forecasting systems: the local behavior data, which we conceptualize as a collection of location-specific historical trajectories.
Complete 3D Human Reconstruction From a Single Incomplete Image
This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e. g., a torso.
Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction
We instead propose fine-grained task that treats each RGB-D pair as a task in our meta-optimization.
Ranked #33 on
Monocular Depth Estimation
on NYU-Depth V2
Strivec: Sparse Tri-Vector Radiance Fields
In contrast to TensoRF which uses a global tensor and focuses on their vector-matrix decomposition, we propose to utilize a cloud of local tensors and apply the classic CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into triple vectors that express local feature distributions along spatial axes and compactly encode a local neural field.
MMVP: Motion-Matrix-based Video Prediction
A central challenge of video prediction lies where the system has to reason the objects' future motions from image frames while simultaneously maintaining the consistency of their appearances across frames.
InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation
To facilitate our investigation for robustness and address limitations of previous works, we collect InSpaceType, a high-quality and high-resolution RGBD dataset for general indoor environments.
Indoor Monocular Depth Estimation
Monocular Depth Estimation
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation
While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored.
