diff --git a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
index bbfbd2e9736a1..397e322a64dea 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Specialize.cpp
@@ -291,6 +291,9 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
   CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcOp);
   CONV_OP_SPECIALIZER(linalg::Conv2DNhwgcGfhwcQOp);
   CONV_OP_SPECIALIZER(linalg::Conv3DOp);
+  CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfOp);
+  CONV_OP_SPECIALIZER(linalg::Conv3DNdhwcDhwcfQOp);
+  CONV_OP_SPECIALIZER(linalg::Conv3DNcdhwFcdhwOp);
   // -----------------------------
   // Depthwise Convolution ops.
   // -----------------------------
@@ -302,6 +305,8 @@ static FailureOr<LinalgOp> specializeLinalgConvolutions(RewriterBase &rewriter,
   CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcQOp);
   CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmOp);
   CONV_OP_SPECIALIZER(linalg::DepthwiseConv2DNhwcHwcmQOp);
+  CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcOp);
+  CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNcdhwCdhwOp);
   CONV_OP_SPECIALIZER(linalg::DepthwiseConv3DNdhwcDhwcmOp);
   // -----------------------------
   // Pooling ops.
diff --git a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
index 1244be90390e2..5c4a359dac4a4 100644
--- a/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Linalg/Utils/Utils.cpp
@@ -569,7 +569,7 @@ class ConvMatcherBuilder {
   }
 
   /// Match body pattern. This should be called last.
-  bool matchBody(bool zeroPointOffset = false) {
+  bool matchBody(bool containsZeroPointOffset = false) {
     if (!matched)
       return false;
     Block *body = op.getBlock();
@@ -577,7 +577,7 @@ class ConvMatcherBuilder {
     switch (poolingType) {
     case PoolingType::None:
       return bodyMatcherForConvolutionOps(yieldOp.getOperand(0), body,
-                                          zeroPointOffset);
+                                          containsZeroPointOffset);
     case PoolingType::MaxSigned:
       return bodyMatcherForMaxSignedPoolOps(yieldOp.getOperand(0), body);
     case PoolingType::MaxUnsigned:
@@ -762,7 +762,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwcHwcfQOp>(
                   /*scalarMap=*/{},
                   /*scalarMap=*/{},
                   /*outputMap=*/{N, H, W, F}})
-      .matchBody(/*zeroPointOffset=*/true);
+      .matchBody(/*containsZeroPointOffset=*/true);
 }
 
 // #inputMap  = affine_map<(N, H, W, F, h, w, c) -> (N, H + h, W + w, c)>
@@ -825,7 +825,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwcFhwcQOp>(
                   /*scalarMap=*/{},
                   /*scalarMap=*/{},
                   /*outputMap=*/{N, H, W, F}})
-      .matchBody(/*zeroPointOffset=*/true);
+      .matchBody(/*containsZeroPointOffset=*/true);
 }
 
 // #inputMap  = affine_map<(N, F, H, W, c, h, w) -> (N, c, H + h, W + w)>
@@ -888,7 +888,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNchwFchwQOp>(
                   /*scalarMap=*/{},
                   /*scalarMap=*/{},
                   /*outputMap=*/{N, F, H, W}})
-      .matchBody(/*zeroPointOffset=*/true);
+      .matchBody(/*containsZeroPointOffset=*/true);
 }
 
 // #inputMap  = affine_map<(N, G, F, H, W, c, h, w) -> (N, G, c, H + h, W + w)>
@@ -987,7 +987,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNgchwGfchwQOp>(
            /*scalarMap=*/{},
            /*scalarMap=*/{},
            /*outputMap=*/{N, G, F, H, W}})
-      .matchBody(/*zeroPointOffset=*/true);
+      .matchBody(/*containsZeroPointOffset=*/true);
 }
 
 // #inputMap  = affine_map<(N, H, W, G, F, h, w, c) -> (N, H + h, W + w, G, c)>
@@ -1054,7 +1054,7 @@ bool isaConvolutionOpOfType<linalg::Conv2DNhwgcGfhwcQOp>(
            /*scalarMap=*/{},
            /*scalarMap=*/{},
            /*outputMap=*/{N, H, W, G, F}})
-      .matchBody(/*zeroPointOffset=*/true);
+      .matchBody(/*containsZeroPointOffset=*/true);
 }
 
 // #inputMap  = affine_map<(D, H, W, d, h, w) -> (D + d, H + h, W + w)>
@@ -1088,6 +1088,114 @@ bool isaConvolutionOpOfType<linalg::Conv3DOp>(LinalgOp op,
       .matchBody();
 }
 
+// #inputMap  = affine_map<(N, D, H, W, F, d, h, w, c)
+//                    -> (N, D + d, H + h, W + w, c)>
+// #filterMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (d, h, w, c, F)>
+// #outputMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (N, D, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNdhwcDhwcfOp>(
+    LinalgOp op, SmallVector<int64_t> *dilations,
+    SmallVector<int64_t> *strides) {
+  if (isa<linalg::Conv3DNdhwcDhwcfOp>(op))
+    return true;
+
+  assert(isaConvolutionOpInterface(op) &&
+         "expected op to implement ConvolutionOpInterface");
+
+  ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+  AffineExpr N = m.dim(0);
+  AffineExpr D = m.dim(1);
+  AffineExpr H = m.dim(2);
+  AffineExpr W = m.dim(3);
+  AffineExpr F = m.dim(4);
+  AffineExpr d = m.dim(5);
+  AffineExpr h = m.dim(6);
+  AffineExpr w = m.dim(7);
+  AffineExpr c = m.dim(8);
+
+  return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+      .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+      .matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/2)
+      .matchMaps({/*inputMap=*/{N, m.strided(D, d, 0), m.strided(H, h, 1),
+                                m.strided(W, w, 2), c},
+                  /*filterMap=*/{d, h, w, c, F},
+                  /*outputMap=*/{N, D, H, W, F}})
+      .matchBody();
+}
+
+// #inputMap  = affine_map<(N, D, H, W, F, d, h, w, c)
+//                    -> (N, D + d, H + h, W + w, c)>
+// #filterMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (d, h, w, c, F)>
+// #scalarMap = affine_map<(N, D, H, W, F, d, h, w, c) -> ()>
+// #outputMap = affine_map<(N, D, H, W, F, d, h, w, c) -> (N, D, H, W, F)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNdhwcDhwcfQOp>(
+    LinalgOp op, SmallVector<int64_t> *dilations,
+    SmallVector<int64_t> *strides) {
+  if (isa<linalg::Conv3DNdhwcDhwcfQOp>(op))
+    return true;
+
+  assert(isaConvolutionOpInterface(op) &&
+         "expected op to implement ConvolutionOpInterface");
+
+  ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+  AffineExpr N = m.dim(0);
+  AffineExpr D = m.dim(1);
+  AffineExpr H = m.dim(2);
+  AffineExpr W = m.dim(3);
+  AffineExpr F = m.dim(4);
+  AffineExpr d = m.dim(5);
+  AffineExpr h = m.dim(6);
+  AffineExpr w = m.dim(7);
+  AffineExpr c = m.dim(8);
+
+  return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+      .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+      .matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/2)
+      .matchMaps({/*inputMap=*/{N, m.strided(D, d, 0), m.strided(H, h, 1),
+                                m.strided(W, w, 2), c},
+                  /*filterMap=*/{d, h, w, c, F},
+                  /*scalarMap=*/{},
+                  /*scalarMap=*/{},
+                  /*outputMap=*/{N, D, H, W, F}})
+      .matchBody(/*containsZeroPointOffset=*/true);
+}
+
+// #inputMap  = affine_map<(N, F, D, H, W, c, d, h, w)
+//                    -> (N, c, D + d, H + h, W + w)>
+// #filterMap = affine_map<(N, F, D, H, W, c, d, h, w) -> (F, c, d, h, w)>
+// #outputMap = affine_map<(N, F, D, H, W, c, d, h, w) -> (N, F, D, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::Conv3DNcdhwFcdhwOp>(
+    LinalgOp op, SmallVector<int64_t> *dilations,
+    SmallVector<int64_t> *strides) {
+  if (isa<linalg::Conv3DNcdhwFcdhwOp>(op))
+    return true;
+
+  assert(isaConvolutionOpInterface(op) &&
+         "expected op to implement ConvolutionOpInterface");
+
+  ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+  AffineExpr N = m.dim(0);
+  AffineExpr F = m.dim(1);
+  AffineExpr D = m.dim(2);
+  AffineExpr H = m.dim(3);
+  AffineExpr W = m.dim(4);
+  AffineExpr c = m.dim(5);
+  AffineExpr d = m.dim(6);
+  AffineExpr h = m.dim(7);
+  AffineExpr w = m.dim(8);
+
+  return m.matchStride(/*iDim=*/2, /*fDim=*/2, /*oDim=*/2, /*idx=*/0)
+      .matchStride(/*iDim=*/3, /*fDim=*/3, /*oDim=*/3, /*idx=*/1)
+      .matchStride(/*iDim=*/4, /*fDim=*/4, /*oDim=*/4, /*idx=*/2)
+      .matchMaps({/*inputMap=*/{N, c, m.strided(D, d, 0), m.strided(H, h, 1),
+                                m.strided(W, w, 2)},
+                  /*filterMap=*/{F, c, d, h, w},
+                  /*outputMap=*/{N, F, D, H, W}})
+      .matchBody();
+}
+
 // #inputMap  = affine_map<(N, W, C, w) -> (N, C, W + w)>
 // #filterMap = affine_map<(N, W, C, w) -> (C, w)>
 // #outputMap = affine_map<(N, W, C, w) -> (N, C, W)>
@@ -1254,7 +1362,7 @@ bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcQOp>(
                   /*scalarMap=*/{},
                   /*scalarMap=*/{},
                   /*outputMap=*/{N, H, W, C}})
-      .matchBody(/*zeroPointOffset=*/true);
+      .matchBody(/*containsZeroPointOffset=*/true);
 }
 
 // #inputMap = affine_map<(N, H, W, C, CM, h, w) -> (N, H + h, W + w, C)>
@@ -1317,7 +1425,76 @@ bool isaConvolutionOpOfType<linalg::DepthwiseConv2DNhwcHwcmQOp>(
                   /*scalarMap=*/{},
                   /*scalarMap=*/{},
                   /*outputMap=*/{N, H, W, C, CM}})
-      .matchBody(/*zeroPointOffset=*/true);
+      .matchBody(/*containsZeroPointOffset=*/true);
+}
+
+// #inputMap = affine_map<(N, D, H, W, d, h, w, C)
+//                -> (N, D + d, H + h, W + w, C)>
+// #filterMap = affine_map<(N, D, H, W, d, h, w, C)
+//                -> (d, h, w, C)>
+// #outputMap = affine_map<(N, D, H, W, d, h, w, C)
+//               -> (N, D, H, W, C)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv3DNdhwcDhwcOp>(
+    LinalgOp op, SmallVector<int64_t> *dilations,
+    SmallVector<int64_t> *strides) {
+  if (isa<linalg::DepthwiseConv3DNdhwcDhwcOp>(op))
+    return true;
+
+  assert(isaConvolutionOpInterface(op) &&
+         "expected op to implement ConvolutionOpInterface");
+
+  ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+  AffineExpr N = m.dim(0);
+  AffineExpr D = m.dim(1);
+  AffineExpr H = m.dim(2);
+  AffineExpr W = m.dim(3);
+  AffineExpr d = m.dim(4);
+  AffineExpr h = m.dim(5);
+  AffineExpr w = m.dim(6);
+  AffineExpr C = m.dim(7);
+
+  return m.matchStride(/*iDim=*/1, /*fDim=*/0, /*oDim=*/1, /*idx=*/0)
+      .matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/1)
+      .matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/2)
+      .matchMaps({/*inputMap=*/{N, m.strided(D, d, 0), m.strided(H, h, 1),
+                                m.strided(W, w, 2), C},
+                  /*filterMap=*/{d, h, w, C},
+                  /*outputMap=*/{N, D, H, W, C}})
+      .matchBody();
+}
+
+// #inputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, C, D + d, H + h, W +
+// w)> #filterMap = affine_map<(N, D, H, W, d, h, w, C) -> (C, d, h, w)>
+// #outputMap = affine_map<(N, D, H, W, d, h, w, C) -> (N, C, D, H, W)>
+template <>
+bool isaConvolutionOpOfType<linalg::DepthwiseConv3DNcdhwCdhwOp>(
+    LinalgOp op, SmallVector<int64_t> *dilations,
+    SmallVector<int64_t> *strides) {
+  if (isa<linalg::DepthwiseConv3DNcdhwCdhwOp>(op))
+    return true;
+
+  assert(isaConvolutionOpInterface(op) &&
+         "expected op to implement ConvolutionOpInterface");
+
+  ConvMatcherBuilder m(op, /*spatialRank=*/3, dilations, strides);
+  AffineExpr N = m.dim(0);
+  AffineExpr D = m.dim(1);
+  AffineExpr H = m.dim(2);
+  AffineExpr W = m.dim(3);
+  AffineExpr d = m.dim(4);
+  AffineExpr h = m.dim(5);
+  AffineExpr w = m.dim(6);
+  AffineExpr C = m.dim(7);
+
+  return m.matchStride(/*iDim=*/2, /*fDim=*/1, /*oDim=*/2, /*idx=*/0)
+      .matchStride(/*iDim=*/3, /*fDim=*/2, /*oDim=*/3, /*idx=*/1)
+      .matchStride(/*iDim=*/4, /*fDim=*/3, /*oDim=*/4, /*idx=*/2)
+      .matchMaps({/*inputMap=*/{N, C, m.strided(D, d, 0), m.strided(H, h, 1),
+                                m.strided(W, w, 2)},
+                  /*filterMap=*/{C, d, h, w},
+                  /*outputMap=*/{N, C, D, H, W}})
+      .matchBody();
 }
 
 // #inputMap = affine_map<(N, D, H, W, CM, d, h, w, C)
diff --git a/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir b/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
index 432fdd12f540d..ac9a33b0528b0 100644
--- a/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
+++ b/mlir/test/Dialect/Linalg/convolution/roundtrip-convolution.mlir
@@ -218,6 +218,45 @@ func.func @conv_3d(%in : tensor<?x?x?xf32>, %filter : tensor<?x?x?xf32>, %out :
 
 // -----
 
+func.func @conv_3d_ndhwc_dhwcf(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+  %0 = linalg.conv_3d_ndhwc_dhwcf
+         {dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>}
+         ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+         outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+  return %0 : tensor<?x?x?x?x?xf32>
+}
+//      CHECK: @conv_3d_ndhwc_dhwcf
+//      CHECK:   linalg.conv_3d_ndhwc_dhwcf
+// CHECK-SAME:      dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>
+
+// -----
+
+func.func @conv_3d_ndhwc_dhwcf_q(%input: tensor<?x?x?x?x?xi8>, %filter: tensor<?x?x?x?x?xi8>, %output: tensor<?x?x?x?x?xi32>, %zp_input: i32, %zp_filter: i32) -> tensor<?x?x?x?x?xi32> {
+  %0 = linalg.conv_3d_ndhwc_dhwcf_q
+         {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}
+         ins (%input, %filter, %zp_input, %zp_filter : tensor<?x?x?x?x?xi8>, tensor<?x?x?x?x?xi8>, i32, i32)
+         outs (%output: tensor<?x?x?x?x?xi32>) -> tensor<?x?x?x?x?xi32>
+  return %0 : tensor<?x?x?x?x?xi32>
+}
+//      CHECK: @conv_3d_ndhwc_dhwcf_q
+//      CHECK:   linalg.conv_3d_ndhwc_dhwcf_q
+// CHECK-SAME:      dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>
+
+// -----
+
+func.func @conv_3d_ncdhw_fcdhw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+  %0 = linalg.conv_3d_ncdhw_fcdhw
+         {dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>}
+         ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?x?xf32>)
+         outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+  return %0 : tensor<?x?x?x?x?xf32>
+}
+//      CHECK: @conv_3d_ncdhw_fcdhw
+//      CHECK:   linalg.conv_3d_ncdhw_fcdhw
+// CHECK-SAME:      dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>
+
+// -----
+
 // -------------------------------
 // Depthwise Convolution ops - 1D.
 // -------------------------------
@@ -334,6 +373,32 @@ func.func @depthwise_conv_2d_nhwc_hwcm_q(%input: tensor<?x?x?x?xi8>, %filter: te
 // Depthwise Convolution ops - 3D.
 // -------------------------------
 
+func.func @depthwise_conv_3d_ndhwc_dhwc(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+  %0 = linalg.depthwise_conv_3d_ndhwc_dhwc
+         {dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>}
+         ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+         outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+  return %0 : tensor<?x?x?x?x?xf32>
+}
+//      CHECK: @depthwise_conv_3d_ndhwc_dhwc
+//      CHECK:   linalg.depthwise_conv_3d_ndhwc_dhwc
+// CHECK-SAME:      dilations = dense<2> : tensor<3xi64>, strides = dense<3> : tensor<3xi64>
+
+// -----
+
+func.func @depthwise_conv_3d_ncdhw_cdhw(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?xf32>, %output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32> {
+  %0 = linalg.depthwise_conv_3d_ncdhw_cdhw
+         {dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>}
+         ins (%input, %filter: tensor<?x?x?x?x?xf32>, tensor<?x?x?x?xf32>)
+         outs (%output: tensor<?x?x?x?x?xf32>) -> tensor<?x?x?x?x?xf32>
+  return %0 : tensor<?x?x?x?x?xf32>
+}
+//      CHECK: @depthwise_conv_3d_ncdhw_cdhw
+//      CHECK:   linalg.depthwise_conv_3d_ncdhw_cdhw
+// CHECK-SAME:      dilations = dense<[1, 2, 3]> : tensor<3xi64>, strides = dense<[4, 5, 6]> : tensor<3xi64>
+
+// -----
+
 func.func @depthwise_conv_3d_ndhwc_dhwcm(%input: tensor<?x?x?x?x?xf32>, %filter: tensor<?x?x?x?x?xf32>, %output: tensor<?x?x?x?x?x?xf32>) -> tensor<?x?x?x?x?x?xf32> {
   %0 = linalg.depthwise_conv_3d_ndhwc_dhwcm
          {dilations = dense<1> : tensor<3xi64>, strides = dense<1> : tensor<3xi64>}