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Found 28 papers, 11 papers with code
Generic Objects as Pose Probes for Few-Shot View Synthesis
COLMAP is frequently employed for preprocessing to estimate poses, while it necessitates a large number of feature matches to operate effectively, and it struggles with scenes characterized by sparse features, large baselines between images, or a limited number of input images.
LLM-enhanced Scene Graph Learning for Household Rearrangement
The household rearrangement task involves spotting misplaced objects in a scene and accommodate them with proper places.
Learning Part-aware 3D Representations by Fusing 2D Gaussians and Superquadrics
In this paper, we introduce a hybrid representation of superquadrics and 2D Gaussians, trying to dig 3D structural clues from multi-view image inputs.
DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems
We propose DISCO, an efficient DIffusion Solver for Combinatorial Optimization problems that excels in both solution quality and inference speed.
Relighting Scenes with Object Insertions in Neural Radiance Fields
The insertion of objects into a scene and relighting are commonly utilized applications in augmented reality (AR).
NeRF-Guided Unsupervised Learning of RGB-D Registration
In this paper, we present NeRF-UR, a novel frame-to-model optimization framework for unsupervised RGB-D registration.
MultiBooth: Towards Generating All Your Concepts in an Image from Text
MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase.
Learning Dual-arm Object Rearrangement for Cartesian Robots
To achieve the goal, the core idea is to develop an effective object-to-arm task assignment strategy for minimizing the cumulative task execution time and maximizing the dual-arm cooperation efficiency.
SuperUDF: Self-supervised UDF Estimation for Surface Reconstruction
The key insight is that if the UDF is estimated correctly, the 3D points should be locally projected onto the underlying surface following the gradient of the UDF.
2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds
The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature description.
Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction
Surface reconstruction from raw point clouds has been studied for decades in the computer graphics community, which is highly demanded by modeling and rendering applications nowadays.
Weakly-supervised Single-view Image Relighting
For re-rendering, we propose a differentiable specular rendering layer to render low-frequency non-Lambertian materials under various illuminations of spherical harmonics.
Learning Accurate Template Matching with Differentiable Coarse-to-Fine Correspondence Refinement
To tackle the challenges, we propose an accurate template matching method based on differentiable coarse-to-fine correspondence refinement.
NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images
We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images.
Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based Composition
Therefore, we propose a novel depth map fusion module to combine the advantages of estimations with multi-resolution inputs.
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry.
DisARM: Displacement Aware Relation Module for 3D Detection
However, adopting relations between all the object or patch proposals for detection is inefficient, and an imbalanced combination of local and global relations brings extra noise that could mislead the training.
Automatic Depression Detection via Learning and Fusing Features from Visual Cues
Depression is one of the most prevalent mental disorders, which seriously affects one's life.
ARM3D: Attention-based relation module for indoor 3D object detection
In the field of 3D object detection, previous methods have been taking the advantage of context encoding, graph embedding, or explicit relation reasoning to extract relation context.
Learning Practically Feasible Policies for Online 3D Bin Packing
In this problem, the items are delivered to the agent without informing the full sequence information.
ROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion
We propose to tackle the difficulties of fast-motion camera tracking in the absence of inertial measurements using random optimization, in particular, the Particle Filter Optimization (PFO).
Online 3D Bin Packing with Constrained Deep Reinforcement Learning
We solve a challenging yet practically useful variant of 3D Bin Packing Problem (3D-BPP).
Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation
Online semantic 3D segmentation in company with real-time RGB-D reconstruction poses special challenges such as how to perform 3D convolution directly over the progressively fused 3D geometric data, and how to smartly fuse information from frame to frame.
Active Scene Understanding via Online Semantic Reconstruction
In our method, the exploratory robot scanning is both driven by and targeting at the recognition and segmentation of semantic objects from the scene.
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts.
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part.
Ranked #16 on
3D Part Segmentation
on ShapeNet-Part
(Class Average IoU metric)
SCORES: Shape Composition with Recursive Substructure Priors
The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss.
Faces as Lighting Probes via Unsupervised Deep Highlight Extraction
We present a method for estimating detailed scene illumination using human faces in a single image.
