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Found 14 papers, 7 papers with code
Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Prior
By rigidly aligning each region with its potential counterpart in the target point cloud, we obtain a region-specific rigid transformation to generate its pseudo flow labels.
Label-Guided Knowledge Distillation for Continual Semantic Segmentation on 2D Images and 3D Point Clouds
To address this issue, we propose a new label-guided knowledge distillation (LGKD) loss, where the old model output is expanded and transplanted (with the guidance of the ground truth label) to form a semantically appropriate class correspondence with the new model output.
Ranked #1 on
Continual Semantic Segmentation
on ScanNet
Weakly Supervised Class-Agnostic Motion Prediction for Autonomous Driving
To this end, we propose a two-stage weakly supervised approach, where the segmentation model trained with the incomplete binary masks in Stage1 will facilitate the self-supervised learning of the motion prediction network in Stage2 by estimating possible moving foregrounds in advance.
Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction
With $G$ as the basic component, we propose a cross consistency learning scheme and a dual reconstruction objective to learn the pose transfer without supervision.
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision
Instance segmentation on 3D point clouds has been attracting increasing attention due to its wide applications, especially in scene understanding areas.
Ranked #22 on
3D Instance Segmentation
on ScanNet(v2)
Efficient Few-Shot Object Detection via Knowledge Inheritance
Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed.
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior
In this work, we focus on scene flow learning on point clouds in a self-supervised manner.
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph Propagation
We propose a novel temporal-spatial framework for effective weakly supervised learning to generate high-quality pseudo labels from these limited annotated data.
3D Pose Transfer with Correspondence Learning and Mesh Refinement
It aims to transfer the pose of a source mesh to a target mesh and keep the identity (e. g., body shape) of the target mesh.
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning
Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs.
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk
Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention.
Self-Supervised Learning
Self-supervised Scene Flow Estimation
HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embedding
Scene flow in 3D point clouds plays an important role in understanding dynamic environments.
Deep attention-based classification network for robust depth prediction
However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?
Monocular Relative Depth Perception With Web Stereo Data Supervision
In this paper we study the problem of monocular relative depth perception in the wild.
