Accurate　and　real-time　object　detection　is　one　of　the　important　functions　for　autonomous　vehicles　to　accurately　perceive　the　surrounding　complex　environment.　Nevertheless,　how　to　get　the　accurate　size,　distance,　position,　posture　and　other　3D　information　of　surrounding　objects　is　a　classic　problem.　3D　object　detection　for　autonomous　driving　has　become　a　popular　research　field　in　recent　years.　Main　research　progress　in　this　field　is　reviewed.　Firstly,　the　characteristics　of　relevant　sensors　in　the　surrounding　environment　of　autonomous　driving　is　introduced.　Then,　the　development　of　object　detection　from　2D　to　3D　is　introduced　and　the　loss　functions　is　applied　for　optimization.　According　to　the　type　of　data　acquired　by　the　sensor,　3D　object　detection　algorithms　is　categorized　into　three　types,　which　are　algorithms　based　on　monocular/stereo　images,　point　clouds,　image　and　point　cloud　fusion.　Futhermore,　the　classic　and　improved　algorithms　for　each　type　of　3D　object　detection　are　reviewed,　analyzed,　and　compared　in　detail.　Simultaneously,　the　mainstream　autonomous　driving　datasets　and　the　evaluation　criteria　of　their　3D　object　detection　algorithms　are　summarized.　Extensive　experiment　results　of　KITTI　and　NuScenes　datasets　are　also　compared　and　analyzed,　which　is　widely　used　inpresent　literature,　summarizing　the　difficulties　and　problems　of　the　existing　algorithms.　Besides,　the　opportunities　and　challenges　of　3D　object　detection　in　data　processing,　feature　extraction　strategy,　multi-sensor　fusion　and　data　distribution　problems　are　proposed　in　hope　of　inspiring　more　future　work.　©　2023　Northeast　University.　All　rights　reserved.