Search Results for author:
Found 37 papers, 20 papers with code
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Invariance and equivariance to the rotation group have been widely discussed in the 3D deep learning community for pointclouds.
Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
Thus the facial radiance field can be flexibly adjusted to the new identity with few reference images.
OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes.
OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments
As a fundamental task of vision-based perception, 3D occupancy prediction reconstructs 3D structures of surrounding environments.
Diffusion-SDF: Text-to-Shape via Voxelized Diffusion
With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e. g. text) has become a hot issue.
Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior
Instead of retraining a costly viewpoint-aware model, we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement.
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos
The generated text prompts are paired with corresponding video clips, and together co-train the text encoder and the video encoder via a contrastive approach.
Ranked #4 on
Action Segmentation
on GTEA
(using extra training data)
Curriculum DeepSDF
When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions.
Semantic Ray: Learning a Generalizable Semantic Field with Cross-Reprojection Attention
In this paper, we aim to learn a semantic radiance field from multiple scenes that is accurate, efficient and generalizable.
IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
The former aims to recover the surface of point cloud through implicit function, while the latter encourages evenly-distributed points.
MonoNeRF: Learning a Generalizable Dynamic Radiance Field from Monocular Videos
More specifically, we learn an implicit velocity field to estimate point trajectory from temporal features with Neural ODE, which is followed by a flow-based feature aggregation module to obtain spatial features along the point trajectory.
SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation
Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene.
Learning Transferable Human-Object Interaction Detector With Natural Language Supervision
More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition.
Graph-Based Social Relation Reasoning
Human beings are fundamentally sociable -- that we generally organize our social lives in terms of relations with other people.
Ranked #1 on
Visual Social Relationship Recognition
on PIPA
Discovering Dynamic Causal Space for DAG Structure Learning
Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
As there is 2D-to-3D ambiguity problem in the viewing direction when extracting 3D flow features from 2D video frames, we consider the volume densities as opacity priors that describe the contributions of flow features to the semantics on the frames.
GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds
In this paper, we propose geometry-to-voxel auxiliary learning to enable voxel representations to access point-level geometric information, which supports better generalisation of the voxel-based backbone with additional interpretations of multi-sensor point clouds.
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network
It results in a serious issue of lagged gradient, making the learned attack at the current step ineffective due to the architecture changes afterward.
Dynamics-aware Adversarial Attack of Adaptive Neural Networks
To address this issue, we propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient.
Image Set Querying Based Localization
Conventional single image based localization methods usually fail to localize a querying image when there exist large variations between the querying image and the pre-built scene.
Deep Adversarial Metric Learning
Learning an effective distance metric between image pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples.
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning
Specifically, we design a deep reinforcement learning model to learn the structure of the graph for bitwise interaction mining, reducing the uncertainty of binary codes by maximizing the mutual information with inputs and related bits, so that the ambiguous bits receive additional instruction from the graph for confident binarization.
Deep Variational Metric Learning
Deep metric learning has been extensively explored recently, which trains a deep neural network to produce discriminative embedding features.
Learning Deep Binary Descriptor With Multi-Quantization
In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching.
Structural Relational Reasoning of Point Clouds
The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to reason for them.
Ranked #46 on
3D Part Segmentation
on ShapeNet-Part
UniformFace: Learning Deep Equidistributed Representation for Face Recognition
In this paper, we propose a new supervision objective named uniform loss to learn deep equidistributed representations for face recognition.
Deep Embedding Learning With Discriminative Sampling Policy
Deep embedding learning aims to learn a distance metric for effective similarity measurement, which has achieved promising performance in various tasks.
Object Pursuit: Building a Space of Objects via Discriminative Weight Generation
We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations.
HyperDet3D: Learning a Scene-conditioned 3D Object Detector
A bathtub in a library, a sink in an office, a bed in a laundry room -- the counter-intuition suggests that scene provides important prior knowledge for 3D object detection, which instructs to eliminate the ambiguous detection of similar objects.
6D Camera Relocalization in Visually Ambiguous Extreme Environments
To this end, we propose: (i) a hierarchical localization system, where we leverage temporal information and (ii) a novel environment-aware image enhancement method to boost the robustness and accuracy.
SEFormer: Structure Embedding Transformer for 3D Object Detection
Effectively preserving and encoding structure features from objects in irregular and sparse LiDAR points is a key challenge to 3D object detection on point cloud.
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment.
Memory-based Adapters for Online 3D Scene Perception
To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability.
Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process through a single image or natural language.
DreamReward: Text-to-3D Generation with Human Preference
Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL), a direct tuning algorithm to optimize the multi-view diffusion models with a redefined scorer.
Learning a Category-level Object Pose Estimator without Pose Annotations
Instead of using manually annotated images, we leverage diffusion models (e. g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images.
