Semi-supervised Implicit Scene Completion from Sparse LiDAR

Paper

Created by Pengfei Li, Yongliang Shi, Tianyu Liu, Hao Zhao, Guyue Zhou and YA-QIN ZHANG from Institute for AI Industry Research(AIR), Tsinghua University.

For complete video, click HERE.

Introduction

Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations. However, this scheme has not yet been successfully used for LiDAR point cloud data, due to its spatially varying sparsity.

In this repository, we develop a novel formulation that conditions the semi-supervised implicit function on localized shape embeddings. It exploits the strong representation learning power of sparse convolutional networks to generate shape-aware dense feature volumes, while still allows semi-supervised signed distance function learning without knowing its exact values at free space. With extensive quantitative and qualitative results, we demonstrate intrinsic properties of this new learning system and its usefulness in real-world road scenes. Notably, we improve IoU from 26.3% to 51.0% on SemanticKITTI. Moreover, we explore two paradigms to integrate semantic label predictions, achieving implicit semantic completion. Codes and data are publicly available.

Citation

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Installation

Requirements

CUDA=11.1
python>=3.8
Pytorch>=1.8
numpy
ninja
MinkowskiEngine
tensorboard
pyyaml
configargparse
scripy
open3d
h5py
plyfile
scikit-image

Clone the repository:

git clone https://github.com/OPEN-AIR-SUN/SISC.git

Data preparation

Download the SemanticKITTI dataset from HERE. Unzip it into the same directory as SISC.

Training and inference

The configuration for training/inference is stored in opt.yaml, which can be modified as needed.

Scene Completion

Run the following command for a certain task (train/valid/visualize):

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 main_sc.py --task=[task] --experiment_name=[experiment_name]

Semantic Scene Completion

SSC option A

Run the following command for a certain task (ssc_pretrain/ssc_valid/train/valid/visualize):

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 main_ssc_a.py --task=[task] --experiment_name=[experiment_name]

Here, use ssc_pretrain/ssc_valid to train/validate the SSC part. Then the pre-trained model can be used to further train the whole model.

SSC option B

Run the following command for a certain task (train/valid/visualize):

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nproc_per_node=1 main_ssc_b.py --task=[task] --experiment_name=[experiment_name]

Model Zoo

Our pre-trained models can be downloaded here:

These models correspond to the ablation study in our paper. The Scale 4 works as our baseline.